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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2207.14140
|
Pravin Game
|
Jerin Paul Selvan, Pravin S. Game
|
Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning
|
5 pages, 7 figures, 3 tables
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
For over a decade now, robotics and the use of artificial agents have become
a common thing.Testing the performance of new path finding or search space
optimization algorithms has also become a challenge as they require simulation
or an environment to test them.The creation of artificial environments with
artificial agents is one of the methods employed to test such algorithms.Games
have also become an environment to test them.The performance of the algorithms
can be compared by using artificial agents that will behave according to the
algorithm in the environment they are put in.The performance parameters can be,
how quickly the agent is able to differentiate between rewarding actions and
hostile actions.This can be tested by placing the agent in an environment with
different types of hurdles and the goal of the agent is to reach the farthest
by taking decisions on actions that will lead to avoiding all the obstacles.The
environment chosen is a game called "Flappy Bird".The goal of the game is to
make the bird fly through a set of pipes of random heights.The bird must go in
between these pipes and must not hit the top, the bottom, or the pipes
themselves.The actions that the bird can take are either to flap its wings or
drop down with gravity.The algorithms that are enforced on the artificial
agents are NeuroEvolution of Augmenting Topologies (NEAT) and Reinforcement
Learning.The NEAT algorithm takes an "N" initial population of artificial
agents.They follow genetic algorithms by considering an objective function,
crossover, mutation, and augmenting topologies.Reinforcement learning, on the
other hand, remembers the state, the action taken at that state, and the reward
received for the action taken using a single agent and a Deep Q-learning
Network.The performance of the NEAT algorithm improves as the initial
population of the artificial agents is increased.
|
[
{
"version": "v1",
"created": "Thu, 28 Jul 2022 15:01:26 GMT"
}
] | 2022-07-29T00:00:00 |
[
[
"Selvan",
"Jerin Paul",
""
],
[
"Game",
"Pravin S.",
""
]
] |
new_dataset
| 0.969128 |
2207.14166
|
Guijie Zhu
|
Guijie Zhu, Zhun Fan, Jiacheng Liu, Duan Yuan, Peili Ma, Meihua Wang,
Weihua Sheng, Kelvin C. P. Wang
|
RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid
Attention Mechanisms for Pavement Crack Segmentation
| null | null | null | null |
cs.CV cs.LG eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The acquisition and evaluation of pavement surface data play an essential
role in pavement condition evaluation. In this paper, an efficient and
effective end-to-end network for automatic pavement crack segmentation, called
RHA-Net, is proposed to improve the pavement crack segmentation accuracy. The
RHA-Net is built by integrating residual blocks (ResBlocks) and hybrid
attention blocks into the encoder-decoder architecture. The ResBlocks are used
to improve the ability of RHA-Net to extract high-level abstract features. The
hybrid attention blocks are designed to fuse both low-level features and
high-level features to help the model focus on correct channels and areas of
cracks, thereby improving the feature presentation ability of RHA-Net. An image
data set containing 789 pavement crack images collected by a self-designed
mobile robot is constructed and used for training and evaluating the proposed
model. Compared with other state-of-the-art networks, the proposed model
achieves better performance and the functionalities of adding residual blocks
and hybrid attention mechanisms are validated in a comprehensive ablation
study. Additionally, a light-weighted version of the model generated by
introducing depthwise separable convolution achieves better a performance and a
much faster processing speed with 1/30 of the number of U-Net parameters. The
developed system can segment pavement crack in real-time on an embedded device
Jetson TX2 (25 FPS). The video taken in real-time experiments is released at
https://youtu.be/3XIogk0fiG4.
|
[
{
"version": "v1",
"created": "Thu, 28 Jul 2022 15:26:01 GMT"
}
] | 2022-07-29T00:00:00 |
[
[
"Zhu",
"Guijie",
""
],
[
"Fan",
"Zhun",
""
],
[
"Liu",
"Jiacheng",
""
],
[
"Yuan",
"Duan",
""
],
[
"Ma",
"Peili",
""
],
[
"Wang",
"Meihua",
""
],
[
"Sheng",
"Weihua",
""
],
[
"Wang",
"Kelvin C. P.",
""
]
] |
new_dataset
| 0.995648 |
2207.14205
|
Chayan Sarkar
|
Pradip Pramanick, Chayan Sarkar, Sayan Paul, Ruddra dev Roychoudhury,
Brojeshwar Bhowmick
|
DoRO: Disambiguation of referred object for embodied agents
|
Accepted in IEEE Robotics & Automation Letters (RA-L)
| null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Robotic task instructions often involve a referred object that the robot must
locate (ground) within the environment. While task intent understanding is an
essential part of natural language understanding, less effort is made to
resolve ambiguity that may arise while grounding the task. Existing works use
vision-based task grounding and ambiguity detection, suitable for a fixed view
and a static robot. However, the problem magnifies for a mobile robot, where
the ideal view is not known beforehand. Moreover, a single view may not be
sufficient to locate all the object instances in the given area, which leads to
inaccurate ambiguity detection. Human intervention is helpful only if the robot
can convey the kind of ambiguity it is facing. In this article, we present DoRO
(Disambiguation of Referred Object), a system that can help an embodied agent
to disambiguate the referred object by raising a suitable query whenever
required. Given an area where the intended object is, DoRO finds all the
instances of the object by aggregating observations from multiple views while
exploring & scanning the area. It then raises a suitable query using the
information from the grounded object instances. Experiments conducted with the
AI2Thor simulator show that DoRO not only detects the ambiguity more accurately
but also raises verbose queries with more accurate information from the
visual-language grounding.
|
[
{
"version": "v1",
"created": "Thu, 28 Jul 2022 16:21:19 GMT"
}
] | 2022-07-29T00:00:00 |
[
[
"Pramanick",
"Pradip",
""
],
[
"Sarkar",
"Chayan",
""
],
[
"Paul",
"Sayan",
""
],
[
"Roychoudhury",
"Ruddra dev",
""
],
[
"Bhowmick",
"Brojeshwar",
""
]
] |
new_dataset
| 0.999739 |
1907.00239
|
Dmitriy Zhuk
|
Dmitriy Zhuk and Barnaby Martin
|
QCSP monsters and the demise of the Chen Conjecture
|
Lemma 17 was retracted and the boundary between co-NP-complete and
PSpace-complete has shifted
| null | null | null |
cs.CC cs.LO math.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We give a surprising classification for the computational complexity of the
Quantified Constraint Satisfaction Problem over a constraint language $\Gamma$,
QCSP$(\Gamma)$, where $\Gamma$ is a finite language over $3$ elements which
contains all constants. In particular, such problems are either in P,
NP-complete, co-NP-complete or PSpace-complete. Our classification refutes the
hitherto widely-believed Chen Conjecture.
Additionally, we show that already on a 4-element domain there exists a
constraint language $\Gamma$ such that QCSP$(\Gamma)$ is DP-complete (from
Boolean Hierarchy), and on a 10-element domain there exists a constraint
language giving the complexity class $\Theta_{2}^{P}$.
Meanwhile, we prove the Chen Conjecture for finite conservative languages
$\Gamma$. If the polymorphism clone of $\Gamma$ has the polynomially generated
powers (PGP) property then QCSP$(\Gamma)$ is in NP. Otherwise, the polymorphism
clone of $\Gamma$ has the exponentially generated powers (EGP) property and
QCSP$(\Gamma)$ is PSpace-complete.
|
[
{
"version": "v1",
"created": "Sat, 29 Jun 2019 17:13:13 GMT"
},
{
"version": "v2",
"created": "Sun, 4 Aug 2019 20:41:38 GMT"
},
{
"version": "v3",
"created": "Sat, 2 Nov 2019 07:35:11 GMT"
},
{
"version": "v4",
"created": "Wed, 27 Jul 2022 07:53:00 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Zhuk",
"Dmitriy",
""
],
[
"Martin",
"Barnaby",
""
]
] |
new_dataset
| 0.960291 |
2107.02317
|
Luis Carlos Garcia-Peraza-Herrera
|
Caspar Gruijthuijsen, Luis C. Garcia-Peraza-Herrera, Gianni Borghesan,
Dominiek Reynaerts, Jan Deprest, Sebastien Ourselin, Tom Vercauteren,
Emmanuel Vander Poorten
|
Robotic Endoscope Control via Autonomous Instrument Tracking
|
Caspar Gruijthuijsen and Luis C. Garcia-Peraza-Herrera have
contributed equally to this work and share first authorship
| null |
10.3389/frobt.2022.832208
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many keyhole interventions rely on bi-manual handling of surgical
instruments, forcing the main surgeon to rely on a second surgeon to act as a
camera assistant. In addition to the burden of excessively involving surgical
staff, this may lead to reduced image stability, increased task completion time
and sometimes errors due to the monotony of the task. Robotic endoscope
holders, controlled by a set of basic instructions, have been proposed as an
alternative, but their unnatural handling may increase the cognitive load of
the (solo) surgeon, which hinders their clinical acceptance. More seamless
integration in the surgical workflow would be achieved if robotic endoscope
holders collaborated with the operating surgeon via semantically rich
instructions that closely resemble instructions that would otherwise be issued
to a human camera assistant, such as "focus on my right-hand instrument". As a
proof of concept, this paper presents a novel system that paves the way towards
a synergistic interaction between surgeons and robotic endoscope holders. The
proposed platform allows the surgeon to perform a bimanual coordination and
navigation task, while a robotic arm autonomously performs the endoscope
positioning tasks. Within our system, we propose a novel tooltip localization
method based on surgical tool segmentation and a novel visual servoing approach
that ensures smooth and appropriate motion of the endoscope camera. We validate
our vision pipeline and run a user study of this system. The clinical relevance
of the study is ensured through the use of a laparoscopic exercise validated by
the European Academy of Gynaecological Surgery which involves bi-manual
coordination and navigation. Successful application of our proposed system
provides a promising starting point towards broader clinical adoption of
robotic endoscope holders.
|
[
{
"version": "v1",
"created": "Mon, 5 Jul 2021 23:24:46 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Dec 2021 20:28:35 GMT"
},
{
"version": "v3",
"created": "Wed, 23 Feb 2022 16:46:46 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Gruijthuijsen",
"Caspar",
""
],
[
"Garcia-Peraza-Herrera",
"Luis C.",
""
],
[
"Borghesan",
"Gianni",
""
],
[
"Reynaerts",
"Dominiek",
""
],
[
"Deprest",
"Jan",
""
],
[
"Ourselin",
"Sebastien",
""
],
[
"Vercauteren",
"Tom",
""
],
[
"Poorten",
"Emmanuel Vander",
""
]
] |
new_dataset
| 0.99462 |
2111.03393
|
Emilio Garcia-Fidalgo
|
Emilio Garcia-Fidalgo, Joan P. Company-Corcoles, Francisco
Bonnin-Pascual, Alberto Ortiz
|
LiODOM: Adaptive Local Mapping for Robust LiDAR-Only Odometry
|
In press
|
Robotics and Autonomous Systems, 2022
| null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the last decades, Light Detection And Ranging (LiDAR) technology has been
extensively explored as a robust alternative for self-localization and mapping.
These approaches typically state ego-motion estimation as a non-linear
optimization problem dependent on the correspondences established between the
current point cloud and a map, whatever its scope, local or global. This paper
proposes LiODOM, a novel LiDAR-only ODOmetry and Mapping approach for pose
estimation and map-building, based on minimizing a loss function derived from a
set of weighted point-to-line correspondences with a local map abstracted from
the set of available point clouds. Furthermore, this work places a particular
emphasis on map representation given its relevance for quick data association.
To efficiently represent the environment, we propose a data structure that
combined with a hashing scheme allows for fast access to any section of the
map. LiODOM is validated by means of a set of experiments on public datasets,
for which it compares favourably against other solutions. Its performance
on-board an aerial platform is also reported.
|
[
{
"version": "v1",
"created": "Fri, 5 Nov 2021 11:07:44 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Jul 2022 12:12:16 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Garcia-Fidalgo",
"Emilio",
""
],
[
"Company-Corcoles",
"Joan P.",
""
],
[
"Bonnin-Pascual",
"Francisco",
""
],
[
"Ortiz",
"Alberto",
""
]
] |
new_dataset
| 0.966579 |
2111.12085
|
Zhengyuan Yang
|
Zhengyuan Yang, Zhe Gan, Jianfeng Wang, Xiaowei Hu, Faisal Ahmed,
Zicheng Liu, Yumao Lu, Lijuan Wang
|
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language
Modeling
|
ECCV 2022 (Oral Presentation)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose UniTAB that Unifies Text And Box outputs for grounded
vision-language (VL) modeling. Grounded VL tasks such as grounded captioning
require the model to generate a text description and align predicted words with
object regions. To achieve this, models must generate desired text and box
outputs together, and meanwhile indicate the alignments between words and
boxes. In contrast to existing solutions that use multiple separate modules for
different outputs, UniTAB represents both text and box outputs with a shared
token sequence, and introduces a special <obj> token to naturally indicate
word-box alignments in the sequence. UniTAB thus could provide a more
comprehensive and interpretable image description, by freely grounding
generated words to object regions. On grounded captioning, UniTAB presents a
simpler solution with a single output head, and significantly outperforms state
of the art in both grounding and captioning evaluations. On general VL tasks
that have different desired output formats (i.e., text, box, or their
combination), UniTAB with a single network achieves better or comparable
performance than task-specific state of the art. Experiments cover 7 VL
benchmarks, including grounded captioning, visual grounding, image captioning,
and visual question answering. Furthermore, UniTAB's unified multi-task network
and the task-agnostic output sequence design make the model parameter efficient
and generalizable to new tasks.
|
[
{
"version": "v1",
"created": "Tue, 23 Nov 2021 18:59:14 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Jul 2022 17:56:35 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Yang",
"Zhengyuan",
""
],
[
"Gan",
"Zhe",
""
],
[
"Wang",
"Jianfeng",
""
],
[
"Hu",
"Xiaowei",
""
],
[
"Ahmed",
"Faisal",
""
],
[
"Liu",
"Zicheng",
""
],
[
"Lu",
"Yumao",
""
],
[
"Wang",
"Lijuan",
""
]
] |
new_dataset
| 0.987474 |
2111.13981
|
Dominic Baril
|
Dominic Baril, Simon-Pierre Desch\^enes, Olivier Gamache, Maxime
Vaidis, Damien LaRocque, Johann Laconte, Vladim\'ir Kubelka, Philippe
Gigu\`ere, Fran\c{c}ois Pomerleau
|
Kilometer-scale autonomous navigation in subarctic forests: challenges
and lessons learned
|
Published in Field Robotics Volume 2. Paper number 50
| null |
10.55417/fr.2022050
| null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Challenges inherent to autonomous wintertime navigation in forests include
lack of reliable a Global Navigation Satellite System (GNSS) signal, low
feature contrast, high illumination variations and changing environment. This
type of off-road environment is an extreme case of situations autonomous cars
could encounter in northern regions. Thus, it is important to understand the
impact of this harsh environment on autonomous navigation systems. To this end,
we present a field report analyzing teach-and-repeat navigation in a subarctic
forest while subject to fluctuating weather, including light and heavy snow,
rain and drizzle. First, we describe the system, which relies on point cloud
registration to localize a mobile robot through a boreal forest, while
simultaneously building a map. We experimentally evaluate this system in over
18.8 km of autonomous navigation in the teach-and-repeat mode. Over 14 repeat
runs, only four manual interventions were required, three of which were due to
localization failure and another one caused by battery power outage. We show
that dense vegetation perturbs the GNSS signal, rendering it unsuitable for
navigation in forest trails. Furthermore, we highlight the increased
uncertainty related to localizing using point cloud registration in forest
trails. We demonstrate that it is not snow precipitation, but snow
accumulation, that affects our system's ability to localize within the
environment. Finally, we expose some challenges and lessons learned from our
field campaign to support better experimental work in winter conditions. Our
dataset is available online.
|
[
{
"version": "v1",
"created": "Sat, 27 Nov 2021 20:39:53 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jul 2022 20:44:10 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Baril",
"Dominic",
""
],
[
"Deschênes",
"Simon-Pierre",
""
],
[
"Gamache",
"Olivier",
""
],
[
"Vaidis",
"Maxime",
""
],
[
"LaRocque",
"Damien",
""
],
[
"Laconte",
"Johann",
""
],
[
"Kubelka",
"Vladimír",
""
],
[
"Giguère",
"Philippe",
""
],
[
"Pomerleau",
"François",
""
]
] |
new_dataset
| 0.996549 |
2203.16875
|
Xiangjun Gao
|
Xiangjun Gao, Jiaolong Yang, Jongyoo Kim, Sida Peng, Zicheng Liu, Xin
Tong
|
MPS-NeRF: Generalizable 3D Human Rendering from Multiview Images
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There has been rapid progress recently on 3D human rendering, including novel
view synthesis and pose animation, based on the advances of neural radiance
fields (NeRF). However, most existing methods focus on person-specific training
and their training typically requires multi-view videos. This paper deals with
a new challenging task -- rendering novel views and novel poses for a person
unseen in training, using only multiview images as input. For this task, we
propose a simple yet effective method to train a generalizable NeRF with
multiview images as conditional input. The key ingredient is a dedicated
representation combining a canonical NeRF and a volume deformation scheme.
Using a canonical space enables our method to learn shared properties of human
and easily generalize to different people. Volume deformation is used to
connect the canonical space with input and target images and query image
features for radiance and density prediction. We leverage the parametric 3D
human model fitted on the input images to derive the deformation, which works
quite well in practice when combined with our canonical NeRF. The experiments
on both real and synthetic data with the novel view synthesis and pose
animation tasks collectively demonstrate the efficacy of our method.
|
[
{
"version": "v1",
"created": "Thu, 31 Mar 2022 08:09:03 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Jul 2022 06:10:50 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Gao",
"Xiangjun",
""
],
[
"Yang",
"Jiaolong",
""
],
[
"Kim",
"Jongyoo",
""
],
[
"Peng",
"Sida",
""
],
[
"Liu",
"Zicheng",
""
],
[
"Tong",
"Xin",
""
]
] |
new_dataset
| 0.990914 |
2204.00833
|
Jing He
|
Jing He, Yiyi Zhou, Qi Zhang, Jun Peng, Yunhang Shen, Xiaoshuai Sun,
Chao Chen, Rongrong Ji
|
PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image
Generation
|
Accepted by ECCV2022. The code is available at
https://github.com/BlingHe/PixelFolder
| null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Pixel synthesis is a promising research paradigm for image generation, which
can well exploit pixel-wise prior knowledge for generation. However, existing
methods still suffer from excessive memory footprint and computation overhead.
In this paper, we propose a progressive pixel synthesis network towards
efficient image generation, coined as PixelFolder. Specifically, PixelFolder
formulates image generation as a progressive pixel regression problem and
synthesizes images via a multi-stage structure, which can greatly reduce the
overhead caused by large tensor transformations. In addition, we introduce
novel pixel folding operations to further improve model efficiency while
maintaining pixel-wise prior knowledge for end-to-end regression. With these
innovative designs, we greatly reduce the expenditure of pixel synthesis, e.g.,
reducing 89% computation and 53% parameters compared with the latest pixel
synthesis method CIPS. To validate our approach, we conduct extensive
experiments on two benchmark datasets, namely FFHQ and LSUN Church. The
experimental results show that with much less expenditure, PixelFolder obtains
new state-of-the-art (SOTA) performance on two benchmark datasets, i.e., 3.77
FID and 2.45 FID on FFHQ and LSUN Church, respectively.Meanwhile, PixelFolder
is also more efficient than the SOTA methods like StyleGAN2, reducing about 72%
computation and 31% parameters, respectively. These results greatly validate
the effectiveness of the proposed PixelFolder.
|
[
{
"version": "v1",
"created": "Sat, 2 Apr 2022 10:55:11 GMT"
},
{
"version": "v2",
"created": "Sun, 5 Jun 2022 06:24:44 GMT"
},
{
"version": "v3",
"created": "Mon, 25 Jul 2022 04:13:03 GMT"
},
{
"version": "v4",
"created": "Wed, 27 Jul 2022 06:40:18 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"He",
"Jing",
""
],
[
"Zhou",
"Yiyi",
""
],
[
"Zhang",
"Qi",
""
],
[
"Peng",
"Jun",
""
],
[
"Shen",
"Yunhang",
""
],
[
"Sun",
"Xiaoshuai",
""
],
[
"Chen",
"Chao",
""
],
[
"Ji",
"Rongrong",
""
]
] |
new_dataset
| 0.999328 |
2204.06681
|
Youngho Kim
|
Jihoon Ryoo, Byungkon Kang, Dongyeob Lee, Seunghyeon Kim, Youngho Kim
|
MINSU (Mobile Inventory And Scanning Unit):Computer Vision and AI
|
Needs to be updated
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The MINSU(Mobile Inventory and Scanning Unit) algorithm uses the
computational vision analysis method to record the residual quantity/fullness
of the cabinet. To do so, it goes through a five-step method: object detection,
foreground subtraction, K-means clustering, percentage estimation, and
counting. The input image goes through the object detection method to analyze
the specific position of the cabinets in terms of coordinates. After doing so,
it goes through the foreground subtraction method to make the image more
focus-able to the cabinet itself by removing the background (some manual work
may have to be done such as selecting the parts that were not grab cut by the
algorithm). In the K-means clustering method, the multi-colored image turns
into a 3 colored monotonous image for quicker and more accurate analysis. At
last, the image goes through percentage estimation and counting. In these two
methods, the proportion that the material inside the cabinet is found in
percentage which then is used to approximate the number of materials inside.
Had this project been successful, the residual quantity management could solve
the problem addressed earlier in the introduction.
|
[
{
"version": "v1",
"created": "Thu, 14 Apr 2022 00:21:14 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jul 2022 12:19:05 GMT"
},
{
"version": "v3",
"created": "Wed, 27 Jul 2022 06:17:15 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Ryoo",
"Jihoon",
""
],
[
"Kang",
"Byungkon",
""
],
[
"Lee",
"Dongyeob",
""
],
[
"Kim",
"Seunghyeon",
""
],
[
"Kim",
"Youngho",
""
]
] |
new_dataset
| 0.99917 |
2206.10708
|
Zhiyang Chen
|
Zhiyang Chen, Sidi Mohamed Beillahi, Fan Long
|
FlashSyn: Flash Loan Attack Synthesis via Counter Example Driven
Approximation
|
29 pages, 8 figures, technical report
| null | null | null |
cs.PL cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
In decentralized finance (DeFi) ecosystem, lenders can offer flash loans to
borrowers, i.e., loans that are only valid within a blockchain transaction and
must be repaid with some fees by the end of that transaction. Unlike normal
loans, flash loans allow borrowers to borrow a large amount of assets without
upfront collaterals deposits. Malicious adversaries can use flash loans to
gather large amount of assets to launch costly exploitations targeting DeFi
protocols. In this paper, we introduce a new framework for automated synthesis
of adversarial contracts that exploit DeFi protocols using flash loans. To
bypass the complexity of a DeFi protocol, we propose a new technique to
approximate the DeFi protocol functional behaviors using numerical methods
(polynomial linear regression and nearest-neighbor interpolation). We then
construct an optimization query using the approximated functions of the DeFi
protocol to find an adversarial attack constituted of a sequence of functions
invocations with optimal parameters that gives the maximum profit. To improve
the accuracy of the approximation, we propose a new counterexamples-driven
approximation refinement technique. We implement our framework in a tool called
FlashSyn. We evaluate FlashSyn on 12 DeFi protocols that were victims to flash
loan attacks and DeFi protocols from Damn Vulnerable DeFi challenges. FlashSyn
automatically synthesizes an adversarial attack for each one of them.
|
[
{
"version": "v1",
"created": "Tue, 21 Jun 2022 19:56:54 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jul 2022 20:52:53 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Chen",
"Zhiyang",
""
],
[
"Beillahi",
"Sidi Mohamed",
""
],
[
"Long",
"Fan",
""
]
] |
new_dataset
| 0.978563 |
2207.10763
|
Yijiong Lin
|
Yijiong Lin, John Lloyd, Alex Church, Nathan F. Lepora
|
Tactile Gym 2.0: Sim-to-real Deep Reinforcement Learning for Comparing
Low-cost High-Resolution Robot Touch
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
High-resolution optical tactile sensors are increasingly used in robotic
learning environments due to their ability to capture large amounts of data
directly relating to agent-environment interaction. However, there is a high
barrier of entry to research in this area due to the high cost of tactile robot
platforms, specialised simulation software, and sim-to-real methods that lack
generality across different sensors. In this letter we extend the Tactile Gym
simulator to include three new optical tactile sensors (TacTip, DIGIT and
DigiTac) of the two most popular types, Gelsight-style (image-shading based)
and TacTip-style (marker based). We demonstrate that a single sim-to-real
approach can be used with these three different sensors to achieve strong
real-world performance despite the significant differences between real tactile
images. Additionally, we lower the barrier of entry to the proposed tasks by
adapting them to an inexpensive 4-DoF robot arm, further enabling the
dissemination of this benchmark. We validate the extended environment on three
physically-interactive tasks requiring a sense of touch: object pushing, edge
following and surface following. The results of our experimental validation
highlight some differences between these sensors, which may help future
researchers select and customize the physical characteristics of tactile
sensors for different manipulations scenarios.
|
[
{
"version": "v1",
"created": "Thu, 21 Jul 2022 21:24:24 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Jul 2022 15:55:19 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Lin",
"Yijiong",
""
],
[
"Lloyd",
"John",
""
],
[
"Church",
"Alex",
""
],
[
"Lepora",
"Nathan F.",
""
]
] |
new_dataset
| 0.968704 |
2207.12585
|
Yijun Yan Dr
|
Jing Geng, Li'e Ma, Xiaoquan Li, Yijun Yan
|
PTGCF: Printing Texture Guided Color Fusion for Impressionism Oil
Painting Style Rendering
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
As a major branch of Non-Photorealistic Rendering (NPR), image stylization
mainly uses the computer algorithms to render a photo into an artistic
painting. Recent work has shown that the extraction of style information such
as stroke texture and color of the target style image is the key to image
stylization. Given its stroke texture and color characteristics, a new stroke
rendering method is proposed, which fully considers the tonal characteristics
and the representative color of the original oil painting, in order to fit the
tone of the original oil painting image into the stylized image and make it
close to the artist's creative effect. The experiments have validated the
efficacy of the proposed model. This method would be more suitable for the
works of pointillism painters with a relatively uniform sense of direction,
especially for natural scenes. When the original painting brush strokes have a
clearer sense of direction, using this method to simulate brushwork texture
features can be less satisfactory.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 00:31:23 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Jul 2022 10:12:12 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Geng",
"Jing",
""
],
[
"Ma",
"Li'e",
""
],
[
"Li",
"Xiaoquan",
""
],
[
"Yan",
"Yijun",
""
]
] |
new_dataset
| 0.998311 |
2207.13147
|
Nofel Yaseen
|
Nofel Yaseen, Liangcheng Yu, Caleb Stanford, Ryan Beckett, Vincent Liu
|
FP4: Line-rate Greybox Fuzz Testing for P4 Switches
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Compared to fixed-function switches, the flexibility of programmable switches
comes at a cost, as programmer mistakes frequently result in subtle bugs in the
network data plane.
In this paper, we present the design and implementation of FP4, a
fuzz-testing framework for P4 switches that achieves high expressiveness,
coverage, and scalability. FP4 directly tests running switches by generating
semi-random input packets and observing their resulting execution in the data
plane. To achieve high coverage and scalability, at runtime, FP4 leverages P4
itself with another "tester" switch that generates and mutates billions of test
packets per second entirely in the dataplane. Because testing some program
branches requires navigating complex semantic input requirements, FP4
additionally leverages the programmability of P4 by instrumenting the tested
program to pass coverage information back to the tester through the packet
header.
We present case studies showing that FP4 can validate both safety and
stateful properties, improves efficiency over existing random packet generation
baselines, and reaches 100% coverage in under a minute on a wide range of
examples.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 18:59:50 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Yaseen",
"Nofel",
""
],
[
"Yu",
"Liangcheng",
""
],
[
"Stanford",
"Caleb",
""
],
[
"Beckett",
"Ryan",
""
],
[
"Liu",
"Vincent",
""
]
] |
new_dataset
| 0.996676 |
2207.13259
|
Wangmeng Xiang
|
Wangmeng Xiang, Chao Li, Biao Wang, Xihan Wei, Xian-Sheng Hua, Lei
Zhang
|
Spatiotemporal Self-attention Modeling with Temporal Patch Shift for
Action Recognition
|
Accepted by ECCV22
| null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transformer-based methods have recently achieved great advancement on 2D
image-based vision tasks. For 3D video-based tasks such as action recognition,
however, directly applying spatiotemporal transformers on video data will bring
heavy computation and memory burdens due to the largely increased number of
patches and the quadratic complexity of self-attention computation. How to
efficiently and effectively model the 3D self-attention of video data has been
a great challenge for transformers. In this paper, we propose a Temporal Patch
Shift (TPS) method for efficient 3D self-attention modeling in transformers for
video-based action recognition. TPS shifts part of patches with a specific
mosaic pattern in the temporal dimension, thus converting a vanilla spatial
self-attention operation to a spatiotemporal one with little additional cost.
As a result, we can compute 3D self-attention using nearly the same computation
and memory cost as 2D self-attention. TPS is a plug-and-play module and can be
inserted into existing 2D transformer models to enhance spatiotemporal feature
learning. The proposed method achieves competitive performance with
state-of-the-arts on Something-something V1 & V2, Diving-48, and Kinetics400
while being much more efficient on computation and memory cost. The source code
of TPS can be found at https://github.com/MartinXM/TPS.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 02:47:07 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Xiang",
"Wangmeng",
""
],
[
"Li",
"Chao",
""
],
[
"Wang",
"Biao",
""
],
[
"Wei",
"Xihan",
""
],
[
"Hua",
"Xian-Sheng",
""
],
[
"Zhang",
"Lei",
""
]
] |
new_dataset
| 0.968407 |
2207.13264
|
Rohan Pratap Singh
|
Rohan Pratap Singh, Iori Kumagai, Antonio Gabas, Mehdi Benallegue,
Yusuke Yoshiyasu, Fumio Kanehiro
|
Instance-specific 6-DoF Object Pose Estimation from Minimal Annotations
|
GitHub code: https://github.com/rohanpsingh/ObjectKeypointTrainer
|
2020 IEEE/SICE International Symposium on System Integration (SII)
|
10.1109/SII46433.2020.9026239
| null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
In many robotic applications, the environment setting in which the 6-DoF pose
estimation of a known, rigid object and its subsequent grasping is to be
performed, remains nearly unchanging and might even be known to the robot in
advance. In this paper, we refer to this problem as instance-specific pose
estimation: the robot is expected to estimate the pose with a high degree of
accuracy in only a limited set of familiar scenarios. Minor changes in the
scene, including variations in lighting conditions and background appearance,
are acceptable but drastic alterations are not anticipated. To this end, we
present a method to rapidly train and deploy a pipeline for estimating the
continuous 6-DoF pose of an object from a single RGB image. The key idea is to
leverage known camera poses and rigid body geometry to partially automate the
generation of a large labeled dataset. The dataset, along with sufficient
domain randomization, is then used to supervise the training of deep neural
networks for predicting semantic keypoints. Experimentally, we demonstrate the
convenience and effectiveness of our proposed method to accurately estimate
object pose requiring only a very small amount of manual annotation for
training.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 03:00:28 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Singh",
"Rohan Pratap",
""
],
[
"Kumagai",
"Iori",
""
],
[
"Gabas",
"Antonio",
""
],
[
"Benallegue",
"Mehdi",
""
],
[
"Yoshiyasu",
"Yusuke",
""
],
[
"Kanehiro",
"Fumio",
""
]
] |
new_dataset
| 0.996716 |
2207.13315
|
Yixuan Fan
|
Yixuan Fan, Zhaopeng Dou, Yali Li, Shengjin Wang
|
Portrait Interpretation and a Benchmark
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a task we name Portrait Interpretation and construct a dataset
named Portrait250K for it. Current researches on portraits such as human
attribute recognition and person re-identification have achieved many
successes, but generally, they: 1) may lack mining the interrelationship
between various tasks and the possible benefits it may bring; 2) design deep
models specifically for each task, which is inefficient; 3) may be unable to
cope with the needs of a unified model and comprehensive perception in actual
scenes. In this paper, the proposed portrait interpretation recognizes the
perception of humans from a new systematic perspective. We divide the
perception of portraits into three aspects, namely Appearance, Posture, and
Emotion, and design corresponding sub-tasks for each aspect. Based on the
framework of multi-task learning, portrait interpretation requires a
comprehensive description of static attributes and dynamic states of portraits.
To invigorate research on this new task, we construct a new dataset that
contains 250,000 images labeled with identity, gender, age, physique, height,
expression, and posture of the whole body and arms. Our dataset is collected
from 51 movies, hence covering extensive diversity. Furthermore, we focus on
representation learning for portrait interpretation and propose a baseline that
reflects our systematic perspective. We also propose an appropriate metric for
this task. Our experimental results demonstrate that combining the tasks
related to portrait interpretation can yield benefits. Code and dataset will be
made public.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 06:25:09 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Fan",
"Yixuan",
""
],
[
"Dou",
"Zhaopeng",
""
],
[
"Li",
"Yali",
""
],
[
"Wang",
"Shengjin",
""
]
] |
new_dataset
| 0.999577 |
2207.13326
|
Daizong Liu
|
Daizong Liu, Wei Hu, Xin Li
|
Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets
Graph Signal Processing
|
arXiv admin note: substantial text overlap with arXiv:2202.07261
| null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the increasing attention in various 3D safety-critical applications,
point cloud learning models have been shown to be vulnerable to adversarial
attacks. Although existing 3D attack methods achieve high success rates, they
delve into the data space with point-wise perturbation, which may neglect the
geometric characteristics. Instead, we propose point cloud attacks from a new
perspective -- the graph spectral domain attack, aiming to perturb graph
transform coefficients in the spectral domain that corresponds to varying
certain geometric structure. Specifically, leveraging on graph signal
processing, we first adaptively transform the coordinates of points onto the
spectral domain via graph Fourier transform (GFT) for compact representation.
Then, we analyze the influence of different spectral bands on the geometric
structure, based on which we propose to perturb the GFT coefficients via a
learnable graph spectral filter. Considering the low-frequency components
mainly contribute to the rough shape of the 3D object, we further introduce a
low-frequency constraint to limit perturbations within imperceptible
high-frequency components. Finally, the adversarial point cloud is generated by
transforming the perturbed spectral representation back to the data domain via
the inverse GFT. Experimental results demonstrate the effectiveness of the
proposed attack in terms of both the imperceptibility and attack success rates.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 07:02:36 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Liu",
"Daizong",
""
],
[
"Hu",
"Wei",
""
],
[
"Li",
"Xin",
""
]
] |
new_dataset
| 0.99567 |
2207.13332
|
Jungo Kasai
|
Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari
Asai, Xinyan Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, Kentaro Inui
|
RealTime QA: What's the Answer Right Now?
|
RealTime QA Website: https://realtimeqa.github.io/
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce RealTime QA, a dynamic question answering (QA) platform that
announces questions and evaluates systems on a regular basis (weekly in this
version). RealTime QA inquires about the current world, and QA systems need to
answer questions about novel events or information. It therefore challenges
static, conventional assumptions in open domain QA datasets and pursues,
instantaneous applications. We build strong baseline models upon large
pretrained language models, including GPT-3 and T5. Our benchmark is an ongoing
effort, and this preliminary report presents real-time evaluation results over
the past month. Our experimental results show that GPT-3 can often properly
update its generation results, based on newly-retrieved documents, highlighting
the importance of up-to-date information retrieval. Nonetheless, we find that
GPT-3 tends to return outdated answers when retrieved documents do not provide
sufficient information to find an answer. This suggests an important avenue for
future research: can an open domain QA system identify such unanswerable cases
and communicate with the user or even the retrieval module to modify the
retrieval results? We hope that RealTime QA will spur progress in instantaneous
applications of question answering and beyond.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 07:26:01 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Kasai",
"Jungo",
""
],
[
"Sakaguchi",
"Keisuke",
""
],
[
"Takahashi",
"Yoichi",
""
],
[
"Bras",
"Ronan Le",
""
],
[
"Asai",
"Akari",
""
],
[
"Yu",
"Xinyan",
""
],
[
"Radev",
"Dragomir",
""
],
[
"Smith",
"Noah A.",
""
],
[
"Choi",
"Yejin",
""
],
[
"Inui",
"Kentaro",
""
]
] |
new_dataset
| 0.998915 |
2207.13370
|
Haoran Xie
|
Mikiya Kusunoki, Shogo Yoshida, Haoran Xie
|
MagGlove: A Haptic Glove with Movable Magnetic Force for Manipulation
Learning
|
4 pages, 8 figures, accepted in the proceedings of Cyberworlds 2022
| null | null | null |
cs.HC cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Recently, haptic gloves have been extensively explored for various practical
applications, such as manipulation learning. Previous glove devices have
different force-driven systems, such as shape memory alloys, servo motors and
pneumatic actuators; however, these proposed devices may have difficulty in
fast finger movement, easy reproduction, and safety issues. In this study, we
propose MagGlove, a novel haptic glove with a movable magnet mechanism that has
a linear motor, to solve these issues. The proposed MagGlove device is a
compact system on the back of the wearer's hand with high responsiveness, ease
of use, and good safety. The proposed device is adaptive with the modification
of the magnitude of the current flowing through the coil. Based on our
evaluation study, it is verified that the proposed device can achieve finger
motion in the given tasks. Therefore, MagGlove can provide flexible support
tailored to the wearers' learning levels in manipulation learning tasks.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 08:54:35 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Kusunoki",
"Mikiya",
""
],
[
"Yoshida",
"Shogo",
""
],
[
"Xie",
"Haoran",
""
]
] |
new_dataset
| 0.999294 |
2207.13419
|
Chintan Patel
|
Chintan Patela, Ali Kashif Bashirb, Ahmad Ali AlZubic, Rutvij H
Jhaveri
|
EBAKE-SE: A Novel ECC Based Authenticated Key Exchange between
Industrial IoT Devices using Secure Element
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Industrial IoT (IIoT) aims to enhance services provided by various industries
such as manufacturing and product processing. IIoT suffers from various
challenges and security is one of the key challenge among those challenges.
Authentication and access control are two notable challenges for any Industrial
IoT (IIoT) based industrial deployment. Any IoT based Industry 4.0 enterprise
designs networks between hundreds of tiny devices such as sensors, actuators,
fog devices and gateways. Thus, articulating a secure authentication protocol
between sensing devices or a sensing device and user devices is an essential
step in IoT security. In this paper, first, we present cryptanalysis for the
certificate-based scheme proposed for similar environment by Das et al. and
prove that their scheme is vulnerable to various traditional attacks such as
device anonymity, MITM, and DoS. We then put forward an inter-device
authentication scheme using an ECC (Elliptic Curve Cryptography) that is highly
secure and lightweight compared to other schemes for a similar environment.
Furthermore, we set forth a formal security analysis using the random oracle
based ROR model and informal security analysis over the Doleve-Yao channel. In
this paper, we present the comparison of the proposed scheme with existing
schemes based on communication cost, computation cost and security index to
prove that the proposed EBAKE-SE is highly efficient, reliable, and trustworthy
compared to other existing schemes for inter-device authentication. At long
last, we present an implementation for the proposed EBAKE-SE using MQTT
protocol
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 09:58:11 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Patela",
"Chintan",
""
],
[
"Bashirb",
"Ali Kashif",
""
],
[
"AlZubic",
"Ahmad Ali",
""
],
[
"Jhaveri",
"Rutvij H",
""
]
] |
new_dataset
| 0.999162 |
2207.13479
|
Xiaojie Jin Mr.
|
Yaojie Shen, Libo Zhang, Kai Xu, Xiaojie Jin
|
AutoTransition: Learning to Recommend Video Transition Effects
|
To appear at ECCV 2022
| null | null | null |
cs.CV cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
Video transition effects are widely used in video editing to connect shots
for creating cohesive and visually appealing videos. However, it is challenging
for non-professionals to choose best transitions due to the lack of
cinematographic knowledge and design skills. In this paper, we present the
premier work on performing automatic video transitions recommendation (VTR):
given a sequence of raw video shots and companion audio, recommend video
transitions for each pair of neighboring shots. To solve this task, we collect
a large-scale video transition dataset using publicly available video templates
on editing softwares. Then we formulate VTR as a multi-modal retrieval problem
from vision/audio to video transitions and propose a novel multi-modal matching
framework which consists of two parts. First we learn the embedding of video
transitions through a video transition classification task. Then we propose a
model to learn the matching correspondence from vision/audio inputs to video
transitions. Specifically, the proposed model employs a multi-modal transformer
to fuse vision and audio information, as well as capture the context cues in
sequential transition outputs. Through both quantitative and qualitative
experiments, we clearly demonstrate the effectiveness of our method. Notably,
in the comprehensive user study, our method receives comparable scores compared
with professional editors while improving the video editing efficiency by
\textbf{300\scalebox{1.25}{$\times$}}. We hope our work serves to inspire other
researchers to work on this new task. The dataset and codes are public at
\url{https://github.com/acherstyx/AutoTransition}.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 12:00:42 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Shen",
"Yaojie",
""
],
[
"Zhang",
"Libo",
""
],
[
"Xu",
"Kai",
""
],
[
"Jin",
"Xiaojie",
""
]
] |
new_dataset
| 0.994539 |
2207.13560
|
Weiqi Li
|
Weiqi Li, Bin Chen, Jian Zhang
|
D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive
Sensing
| null | null | null | null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Mapping optimization algorithms into neural networks, deep unfolding networks
(DUNs) have achieved impressive success in compressive sensing (CS). From the
perspective of optimization, DUNs inherit a well-defined and interpretable
structure from iterative steps. However, from the viewpoint of neural network
design, most existing DUNs are inherently established based on traditional
image-domain unfolding, which takes one-channel images as inputs and outputs
between adjacent stages, resulting in insufficient information transmission
capability and inevitable loss of the image details. In this paper, to break
the above bottleneck, we first propose a generalized dual-domain optimization
framework, which is general for inverse imaging and integrates the merits of
both (1) image-domain and (2) convolutional-coding-domain priors to constrain
the feasible region in the solution space. By unfolding the proposed framework
into deep neural networks, we further design a novel Dual-Domain Deep
Convolutional Coding Network (D3C2-Net) for CS imaging with the capability of
transmitting high-throughput feature-level image representation through all the
unfolded stages. Experiments on natural and MR images demonstrate that our
D3C2-Net achieves higher performance and better accuracy-complexity trade-offs
than other state-of-the-arts.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 14:52:32 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Li",
"Weiqi",
""
],
[
"Chen",
"Bin",
""
],
[
"Zhang",
"Jian",
""
]
] |
new_dataset
| 0.971029 |
2207.13638
|
Muhammed Yusuf Ozkaya
|
M. Yusuf \"Ozkaya and \"Umit V. \c{C}ataly\"urek
|
A Simple and Elegant Mathematical Formulation for the Acyclic DAG
Partitioning Problem
|
10+2 pages, 1 figure (4 subfigures)
| null | null | null |
cs.DS cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work addresses the NP-Hard problem of acyclic directed acyclic graph
(DAG) partitioning problem. The acyclic partitioning problem is defined as
partitioning the vertex set of a given directed acyclic graph into disjoint and
collectively exhaustive subsets (parts). Parts are to be assigned such that the
total sum of the vertex weights within each part satisfies a common upper bound
and the total sum of the edge costs that connect nodes across different parts
is minimized. Additionally, the quotient graph, i.e., the induced graph where
all nodes that are assigned to the same part are contracted to a single node
and edges of those are replaced with cumulative edges towards other nodes, is
also a directed acyclic graph. That is, the quotient graph itself is also a
graph that contains no cycles. Many computational and real-life applications
such as in computational task scheduling, RTL simulations, scheduling of
rail-rail transshipment tasks and Very Large Scale Integration (VLSI) design
make use of acyclic DAG partitioning. We address the need for a simple and
elegant mathematical formulation for the acyclic DAG partitioning problem that
enables easier understanding, communication, implementation, and
experimentation on the problem.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 16:57:00 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Özkaya",
"M. Yusuf",
""
],
[
"Çatalyürek",
"Ümit V.",
""
]
] |
new_dataset
| 0.995154 |
2207.13642
|
Nishant Kumar
|
Nishant Kumar, Sudhan Majhi, and A.K. Upadhyay
|
A Direct Construction of Complete Complementary Code with Zero
Correlation Zone property for Prime-Power Length
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we propose a direct construction of a novel type of code set,
which has combined properties of complete complementary code (CCC) and
zero-correlation zone (ZCZ) sequences and called it complete complementary-ZCZ
(CC-ZCZ) code set. The code set is constructed by using multivariable
functions. The proposed construction also provides Golay-ZCZ codes with new
lengths, i.e., prime-power lengths. The proposed Golay-ZCZ codes are optimal
and asymptotically optimal for binary and non-binary cases, respectively, by
\emph{Tang-Fan-Matsufuzi} bound. Furthermore, the proposed direct construction
provides novel ZCZ sequences of length $p^k$, where $k$ is an integer $\geq 2$.
We establish a relationship between the proposed CC-ZCZ code set and the
first-order generalized Reed-Muller (GRM) code, and proved that both have the
same Hamming distance. We also counted the number of CC-ZCZ code set in
first-order GRM codes. The column sequence peak-to-mean envelope power ratio
(PMEPR) of the proposed CC-ZCZ construction is derived and compared with
existing works. The proposed construction is also deduced to Golay-ZCZ and ZCZ
sequences which are compared to the existing work. The proposed construction
generalizes many of the existing work.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 17:02:23 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Kumar",
"Nishant",
""
],
[
"Majhi",
"Sudhan",
""
],
[
"Upadhyay",
"A. K.",
""
]
] |
new_dataset
| 0.995941 |
2207.13648
|
Rushit Dave
|
Zachary Deridder, Nyle Siddiqui, Thomas Reither, Rushit Dave, Brendan
Pelto, Naeem Seliya, Mounika Vanamala
|
Continuous User Authentication Using Machine Learning and Multi-Finger
Mobile Touch Dynamics with a Novel Dataset
| null | null | null | null |
cs.HC cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
As technology grows and evolves rapidly, it is increasingly clear that mobile
devices are more commonly used for sensitive matters than ever before. A need
to authenticate users continuously is sought after as a single-factor or multi
factor authentication may only initially validate a user, which does not help
if an impostor can bypass this initial validation. The field of touch dynamics
emerges as a clear way to non intrusively collect data about a user and their
behaviors in order to develop and make imperative security related decisions in
real time. In this paper we present a novel dataset consisting of tracking 25
users playing two mobile games Snake.io and Minecraft each for 10 minutes,
along with their relevant gesture data. From this data, we ran machine learning
binary classifiers namely Random Forest and K Nearest Neighbor to attempt to
authenticate whether a sample of a particular users actions were genuine. Our
strongest model returned an average accuracy of roughly 93% for both games,
showing touch dynamics can differentiate users effectively and is a feasible
consideration for authentication schemes. Our dataset can be observed at
https://github.com/zderidder/MC-Snake-Results
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 17:10:03 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Deridder",
"Zachary",
""
],
[
"Siddiqui",
"Nyle",
""
],
[
"Reither",
"Thomas",
""
],
[
"Dave",
"Rushit",
""
],
[
"Pelto",
"Brendan",
""
],
[
"Seliya",
"Naeem",
""
],
[
"Vanamala",
"Mounika",
""
]
] |
new_dataset
| 0.99984 |
2207.13685
|
Alan C. Calder
|
Alan C. Calder, Catherine Feldman, Eva Siegmann, John Dey, Anthony
Curtis, Smeet Chheda, Robert J. Harrison
|
On Using Linux Kernel Huge Pages with FLASH, an Astrophysical Simulation
Code
|
6 pages, 1 figure, accepted to Embracing Arm for HPC, An IEEE Cluster
2022 Workshop
| null | null | null |
cs.DC astro-ph.HE astro-ph.IM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present efforts at improving the performance of FLASH, a multi-scale,
multi-physics simulation code principally for astrophysical applications, by
using huge pages on Ookami, an HPE Apollo 80 A64FX platform. FLASH is written
principally in modern Fortran and makes use of the PARAMESH library to manage a
block-structured adaptive mesh. We explored options for enabling the use of
huge pages with several compilers, but we were only able to successfully use
huge pages when compiling with the Fujitsu compiler. The use of huge pages
substantially reduced the number of translation lookaside buffer misses, but
overall performance gains were marginal.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 17:55:01 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Calder",
"Alan C.",
""
],
[
"Feldman",
"Catherine",
""
],
[
"Siegmann",
"Eva",
""
],
[
"Dey",
"John",
""
],
[
"Curtis",
"Anthony",
""
],
[
"Chheda",
"Smeet",
""
],
[
"Harrison",
"Robert J.",
""
]
] |
new_dataset
| 0.991577 |
2207.13691
|
Muhammad Zubair Irshad
|
Muhammad Zubair Irshad, Sergey Zakharov, Rares Ambrus, Thomas Kollar,
Zsolt Kira, Adrien Gaidon
|
ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and
Pose Optimization
|
Accepted to European Conference on Computer Vision (ECCV), 2022
| null | null | null |
cs.CV cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Our method studies the complex task of object-centric 3D understanding from a
single RGB-D observation. As it is an ill-posed problem, existing methods
suffer from low performance for both 3D shape and 6D pose and size estimation
in complex multi-object scenarios with occlusions. We present ShAPO, a method
for joint multi-object detection, 3D textured reconstruction, 6D object pose
and size estimation. Key to ShAPO is a single-shot pipeline to regress shape,
appearance and pose latent codes along with the masks of each object instance,
which is then further refined in a sparse-to-dense fashion. A novel
disentangled shape and appearance database of priors is first learned to embed
objects in their respective shape and appearance space. We also propose a
novel, octree-based differentiable optimization step, allowing us to further
improve object shape, pose and appearance simultaneously under the learned
latent space, in an analysis-by-synthesis fashion. Our novel joint implicit
textured object representation allows us to accurately identify and reconstruct
novel unseen objects without having access to their 3D meshes. Through
extensive experiments, we show that our method, trained on simulated indoor
scenes, accurately regresses the shape, appearance and pose of novel objects in
the real-world with minimal fine-tuning. Our method significantly out-performs
all baselines on the NOCS dataset with an 8% absolute improvement in mAP for 6D
pose estimation. Project page:
https://zubair-irshad.github.io/projects/ShAPO.html
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 17:59:31 GMT"
}
] | 2022-07-28T00:00:00 |
[
[
"Irshad",
"Muhammad Zubair",
""
],
[
"Zakharov",
"Sergey",
""
],
[
"Ambrus",
"Rares",
""
],
[
"Kollar",
"Thomas",
""
],
[
"Kira",
"Zsolt",
""
],
[
"Gaidon",
"Adrien",
""
]
] |
new_dataset
| 0.988696 |
2108.05563
|
Joshua Rego
|
Joshua D. Rego, Huaijin Chen, Shuai Li, Jinwei Gu, Suren Jayasuriya
|
Deep Camera Obscura: An Image Restoration Pipeline for Lensless Pinhole
Photography
|
11 pages, 10 figures
| null |
10.1364/OE.460636
| null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The lensless pinhole camera is perhaps the earliest and simplest form of an
imaging system using only a pinhole-sized aperture in place of a lens. They can
capture an infinite depth-of-field and offer greater freedom from optical
distortion over their lens-based counterparts. However, the inherent
limitations of a pinhole system result in lower sharpness from blur caused by
optical diffraction and higher noise levels due to low light throughput of the
small aperture, requiring very long exposure times to capture well-exposed
images. In this paper, we explore an image restoration pipeline using deep
learning and domain-knowledge of the pinhole system to enhance the pinhole
image quality through a joint denoise and deblur approach. Our approach allows
for more practical exposure times for hand-held photography and provides higher
image quality, making it more suitable for daily photography compared to other
lensless cameras while keeping size and cost low. This opens up the potential
of pinhole cameras to be used in smaller devices, such as smartphones.
|
[
{
"version": "v1",
"created": "Thu, 12 Aug 2021 07:03:00 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Rego",
"Joshua D.",
""
],
[
"Chen",
"Huaijin",
""
],
[
"Li",
"Shuai",
""
],
[
"Gu",
"Jinwei",
""
],
[
"Jayasuriya",
"Suren",
""
]
] |
new_dataset
| 0.987165 |
2108.08719
|
Morteza Alipourlangouri
|
Morteza Alipourlangouri, Adam Mansfield, Fei Chiang, Yinghui Wu
|
Temporal Graph Functional Dependencies [Extended Version]
| null | null | null | null |
cs.DB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Data dependencies have been extended to graphs to characterize topological
and value constraints. Existing data dependencies are defined to capture
inconsistencies in static graphs. Nevertheless, inconsistencies may occur over
evolving graphs and only for certain time periods. The need for capturing such
inconsistencies in temporal graphs is evident in anomaly detection and
predictive dynamic network analysis. This paper introduces a class of data
dependencies called Temporal Graph Functional Dependencies (TGFDs). TGFDs
generalize functional dependencies to temporal graphs as a sequence of graph
snapshots that are induced by time intervals, and enforce both topological
constraints and attribute value dependencies that must be satisfied by these
snapshots. (1) We establish the complexity results for the satisfiability and
implication problems of TGFDs. (2) We propose a sound and complete
axiomatization system for TGFDs. (3) We also present efficient parallel
algorithms to detect inconsistencies in temporal graphs as violations of TGFDs.
The algorithm exploits data and temporal locality induced by time intervals,
and uses incremental pattern matching and load balancing strategies to enable
feasible error detection in large temporal graphs. Using real datasets, we
experimentally verify that our algorithms achieve lower runtimes compared to
existing baselines, while improving the accuracy over error detection using
existing graph data constraints, e.g., GFDs and GTARs with 55% and 74% gain in
F1-score, respectively.
|
[
{
"version": "v1",
"created": "Thu, 19 Aug 2021 14:40:37 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Jan 2022 01:21:09 GMT"
},
{
"version": "v3",
"created": "Wed, 27 Apr 2022 23:41:18 GMT"
},
{
"version": "v4",
"created": "Mon, 9 May 2022 03:05:46 GMT"
},
{
"version": "v5",
"created": "Tue, 26 Jul 2022 02:14:40 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Alipourlangouri",
"Morteza",
""
],
[
"Mansfield",
"Adam",
""
],
[
"Chiang",
"Fei",
""
],
[
"Wu",
"Yinghui",
""
]
] |
new_dataset
| 0.99877 |
2109.06325
|
Jacopo Panerati
|
Zhaocong Yuan, Adam W. Hall, Siqi Zhou, Lukas Brunke, Melissa Greeff,
Jacopo Panerati, Angela P. Schoellig (University of Toronto Institute for
Aerospace Studies, University of Toronto Robotics Institute, Vector Institute
for Artificial Intelligence)
|
safe-control-gym: a Unified Benchmark Suite for Safe Learning-based
Control and Reinforcement Learning in Robotics
|
8 pages, 8 figures
| null | null | null |
cs.RO cs.LG cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, both reinforcement learning and learning-based control -- as
well as the study of their safety, which is crucial for deployment in
real-world robots -- have gained significant traction. However, to adequately
gauge the progress and applicability of new results, we need the tools to
equitably compare the approaches proposed by the controls and reinforcement
learning communities. Here, we propose a new open-source benchmark suite,
called safe-control-gym, supporting both model-based and data-based control
techniques. We provide implementations for three dynamic systems -- the
cart-pole, the 1D, and 2D quadrotor -- and two control tasks -- stabilization
and trajectory tracking. We propose to extend OpenAI's Gym API -- the de facto
standard in reinforcement learning research -- with (i) the ability to specify
(and query) symbolic dynamics and (ii) constraints, and (iii) (repeatably)
inject simulated disturbances in the control inputs, state measurements, and
inertial properties. To demonstrate our proposal and in an attempt to bring
research communities closer together, we show how to use safe-control-gym to
quantitatively compare the control performance, data efficiency, and safety of
multiple approaches from the fields of traditional control, learning-based
control, and reinforcement learning.
|
[
{
"version": "v1",
"created": "Mon, 13 Sep 2021 21:09:28 GMT"
},
{
"version": "v2",
"created": "Sat, 18 Sep 2021 20:05:26 GMT"
},
{
"version": "v3",
"created": "Fri, 25 Feb 2022 16:12:56 GMT"
},
{
"version": "v4",
"created": "Tue, 26 Jul 2022 11:49:36 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Yuan",
"Zhaocong",
"",
"University of Toronto Institute for\n Aerospace Studies, University of Toronto Robotics Institute, Vector Institute\n for Artificial Intelligence"
],
[
"Hall",
"Adam W.",
"",
"University of Toronto Institute for\n Aerospace Studies, University of Toronto Robotics Institute, Vector Institute\n for Artificial Intelligence"
],
[
"Zhou",
"Siqi",
"",
"University of Toronto Institute for\n Aerospace Studies, University of Toronto Robotics Institute, Vector Institute\n for Artificial Intelligence"
],
[
"Brunke",
"Lukas",
"",
"University of Toronto Institute for\n Aerospace Studies, University of Toronto Robotics Institute, Vector Institute\n for Artificial Intelligence"
],
[
"Greeff",
"Melissa",
"",
"University of Toronto Institute for\n Aerospace Studies, University of Toronto Robotics Institute, Vector Institute\n for Artificial Intelligence"
],
[
"Panerati",
"Jacopo",
"",
"University of Toronto Institute for\n Aerospace Studies, University of Toronto Robotics Institute, Vector Institute\n for Artificial Intelligence"
],
[
"Schoellig",
"Angela P.",
"",
"University of Toronto Institute for\n Aerospace Studies, University of Toronto Robotics Institute, Vector Institute\n for Artificial Intelligence"
]
] |
new_dataset
| 0.99932 |
2111.03845
|
Qinghui Liu
|
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen and Arnt-B{\o}rre
Salberg
|
Multi-modal land cover mapping of remote sensing images using pyramid
attention and gated fusion networks
|
24 pages, 11 figures, submitted to IJRS
| null |
10.1080/01431161.2022.2098078
| null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Multi-modality data is becoming readily available in remote sensing (RS) and
can provide complementary information about the Earth's surface. Effective
fusion of multi-modal information is thus important for various applications in
RS, but also very challenging due to large domain differences, noise, and
redundancies. There is a lack of effective and scalable fusion techniques for
bridging multiple modality encoders and fully exploiting complementary
information. To this end, we propose a new multi-modality network (MultiModNet)
for land cover mapping of multi-modal remote sensing data based on a novel
pyramid attention fusion (PAF) module and a gated fusion unit (GFU). The PAF
module is designed to efficiently obtain rich fine-grained contextual
representations from each modality with a built-in cross-level and cross-view
attention fusion mechanism, and the GFU module utilizes a novel gating
mechanism for early merging of features, thereby diminishing hidden
redundancies and noise. This enables supplementary modalities to effectively
extract the most valuable and complementary information for late feature
fusion. Extensive experiments on two representative RS benchmark datasets
demonstrate the effectiveness, robustness, and superiority of the MultiModNet
for multi-modal land cover classification.
|
[
{
"version": "v1",
"created": "Sat, 6 Nov 2021 10:01:01 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Liu",
"Qinghui",
""
],
[
"Kampffmeyer",
"Michael",
""
],
[
"Jenssen",
"Robert",
""
],
[
"Salberg",
"Arnt-Børre",
""
]
] |
new_dataset
| 0.991597 |
2111.09999
|
Bao Doan
|
Bao Gia Doan, Minhui Xue, Shiqing Ma, Ehsan Abbasnejad, Damith C.
Ranasinghe
|
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep
Neural Network Systems
|
Accepted for publication in the IEEE Transactions on Information
Forensics & Security (TIFS)
| null | null | null |
cs.CV cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Deep neural networks are vulnerable to attacks from adversarial inputs and,
more recently, Trojans to misguide or hijack the model's decision. We expose
the existence of an intriguing class of spatially bounded, physically
realizable, adversarial examples -- Universal NaTuralistic adversarial paTches
-- we call TnTs, by exploring the superset of the spatially bounded adversarial
example space and the natural input space within generative adversarial
networks. Now, an adversary can arm themselves with a patch that is
naturalistic, less malicious-looking, physically realizable, highly effective
achieving high attack success rates, and universal. A TnT is universal because
any input image captured with a TnT in the scene will: i) misguide a network
(untargeted attack); or ii) force the network to make a malicious decision
(targeted attack). Interestingly, now, an adversarial patch attacker has the
potential to exert a greater level of control -- the ability to choose a
location-independent, natural-looking patch as a trigger in contrast to being
constrained to noisy perturbations -- an ability is thus far shown to be only
possible with Trojan attack methods needing to interfere with the model
building processes to embed a backdoor at the risk discovery; but, still
realize a patch deployable in the physical world. Through extensive experiments
on the large-scale visual classification task, ImageNet with evaluations across
its entire validation set of 50,000 images, we demonstrate the realistic threat
from TnTs and the robustness of the attack. We show a generalization of the
attack to create patches achieving higher attack success rates than existing
state-of-the-art methods. Our results show the generalizability of the attack
to different visual classification tasks (CIFAR-10, GTSRB, PubFig) and multiple
state-of-the-art deep neural networks such as WideResnet50, Inception-V3 and
VGG-16.
|
[
{
"version": "v1",
"created": "Fri, 19 Nov 2021 01:35:10 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jul 2022 02:21:11 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Doan",
"Bao Gia",
""
],
[
"Xue",
"Minhui",
""
],
[
"Ma",
"Shiqing",
""
],
[
"Abbasnejad",
"Ehsan",
""
],
[
"Ranasinghe",
"Damith C.",
""
]
] |
new_dataset
| 0.997539 |
2201.07459
|
John Seon Keun Yi
|
John Seon Keun Yi, Minseok Seo, Jongchan Park, Dong-Geol Choi
|
PT4AL: Using Self-Supervised Pretext Tasks for Active Learning
|
Code is available at https://github.com/johnsk95/PT4AL Updated for
ECCV 2022 submission
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Labeling a large set of data is expensive. Active learning aims to tackle
this problem by asking to annotate only the most informative data from the
unlabeled set. We propose a novel active learning approach that utilizes
self-supervised pretext tasks and a unique data sampler to select data that are
both difficult and representative. We discover that the loss of a simple
self-supervised pretext task, such as rotation prediction, is closely
correlated to the downstream task loss. Before the active learning iterations,
the pretext task learner is trained on the unlabeled set, and the unlabeled
data are sorted and split into batches by their pretext task losses. In each
active learning iteration, the main task model is used to sample the most
uncertain data in a batch to be annotated. We evaluate our method on various
image classification and segmentation benchmarks and achieve compelling
performances on CIFAR10, Caltech-101, ImageNet, and Cityscapes. We further show
that our method performs well on imbalanced datasets, and can be an effective
solution to the cold-start problem where active learning performance is
affected by the randomly sampled initial labeled set.
|
[
{
"version": "v1",
"created": "Wed, 19 Jan 2022 07:58:06 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jun 2022 13:07:47 GMT"
},
{
"version": "v3",
"created": "Tue, 26 Jul 2022 09:21:37 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Yi",
"John Seon Keun",
""
],
[
"Seo",
"Minseok",
""
],
[
"Park",
"Jongchan",
""
],
[
"Choi",
"Dong-Geol",
""
]
] |
new_dataset
| 0.977328 |
2203.00146
|
Jennie Rogers
|
Jennie Rogers, Elizabeth Adetoro, Johes Bater, Talia Canter, Dong Fu,
Andrew Hamilton, Amro Hassan, Ashley Martinez, Erick Michalski, Vesna
Mitrovic, Fred Rachman, Raj Shah, Matt Sterling, Kyra VanDoren, Theresa L.
Walunas, Xiao Wang, and Abel Kho
|
VaultDB: A Real-World Pilot of Secure Multi-Party Computation within a
Clinical Research Network
| null | null | null | null |
cs.DB cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Electronic health records represent a rich and growing source of clinical
data for research. Privacy, regulatory, and institutional concerns limit the
speed and ease of sharing this data. VaultDB is a framework for securely
computing SQL queries over private data from two or more sources. It evaluates
queries using secure multiparty computation: cryptographic protocols that
evaluate a function such that the only information revealed from running it is
the query answer. We describe the development of a HIPAA-compliant version of
VaultDB on the Chicago Area Patient Centered Outcomes Research Network
(CAPriCORN). This multi-institutional clinical research network spans the
electronic health records of nearly 13M patients over hundreds of clinics and
hospitals in the Chicago metropolitan area. Our results from deploying at three
health systems within this network show its efficiency and scalability for
distributed clinical research analyses without moving patient records from
their site of origin.
|
[
{
"version": "v1",
"created": "Mon, 28 Feb 2022 23:56:59 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2022 21:48:54 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Rogers",
"Jennie",
""
],
[
"Adetoro",
"Elizabeth",
""
],
[
"Bater",
"Johes",
""
],
[
"Canter",
"Talia",
""
],
[
"Fu",
"Dong",
""
],
[
"Hamilton",
"Andrew",
""
],
[
"Hassan",
"Amro",
""
],
[
"Martinez",
"Ashley",
""
],
[
"Michalski",
"Erick",
""
],
[
"Mitrovic",
"Vesna",
""
],
[
"Rachman",
"Fred",
""
],
[
"Shah",
"Raj",
""
],
[
"Sterling",
"Matt",
""
],
[
"VanDoren",
"Kyra",
""
],
[
"Walunas",
"Theresa L.",
""
],
[
"Wang",
"Xiao",
""
],
[
"Kho",
"Abel",
""
]
] |
new_dataset
| 0.997801 |
2205.05677
|
Soshi Shimada
|
Soshi Shimada, Vladislav Golyanik, Zhi Li, Patrick P\'erez, Weipeng
Xu, Christian Theobalt
|
HULC: 3D Human Motion Capture with Pose Manifold Sampling and Dense
Contact Guidance
| null | null | null | null |
cs.CV cs.GR cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Marker-less monocular 3D human motion capture (MoCap) with scene interactions
is a challenging research topic relevant for extended reality, robotics and
virtual avatar generation. Due to the inherent depth ambiguity of monocular
settings, 3D motions captured with existing methods often contain severe
artefacts such as incorrect body-scene inter-penetrations, jitter and body
floating. To tackle these issues, we propose HULC, a new approach for 3D human
MoCap which is aware of the scene geometry. HULC estimates 3D poses and dense
body-environment surface contacts for improved 3D localisations, as well as the
absolute scale of the subject. Furthermore, we introduce a 3D pose trajectory
optimisation based on a novel pose manifold sampling that resolves erroneous
body-environment inter-penetrations. Although the proposed method requires less
structured inputs compared to existing scene-aware monocular MoCap algorithms,
it produces more physically-plausible poses: HULC significantly and
consistently outperforms the existing approaches in various experiments and on
different metrics. Project page: https://vcai.mpi-inf.mpg.de/projects/HULC/.
|
[
{
"version": "v1",
"created": "Wed, 11 May 2022 17:59:31 GMT"
},
{
"version": "v2",
"created": "Mon, 23 May 2022 16:26:38 GMT"
},
{
"version": "v3",
"created": "Tue, 24 May 2022 10:34:20 GMT"
},
{
"version": "v4",
"created": "Tue, 26 Jul 2022 08:17:40 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Shimada",
"Soshi",
""
],
[
"Golyanik",
"Vladislav",
""
],
[
"Li",
"Zhi",
""
],
[
"Pérez",
"Patrick",
""
],
[
"Xu",
"Weipeng",
""
],
[
"Theobalt",
"Christian",
""
]
] |
new_dataset
| 0.998799 |
2207.00401
|
Jorge F. Lazo
|
Jorge F. Lazo and Chun-Feng Lai and Sara Moccia and Benoit Rosa and
Michele Catellani and Michel de Mathelin and Giancarlo Ferrigno and Paul
Breedveld and Jenny Dankelman and Elena De Momi
|
Autonomous Intraluminal Navigation of a Soft Robot using
Deep-Learning-based Visual Servoing
| null | null | null | null |
cs.RO cs.AI cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Navigation inside luminal organs is an arduous task that requires
non-intuitive coordination between the movement of the operator's hand and the
information obtained from the endoscopic video. The development of tools to
automate certain tasks could alleviate the physical and mental load of doctors
during interventions, allowing them to focus on diagnosis and decision-making
tasks. In this paper, we present a synergic solution for intraluminal
navigation consisting of a 3D printed endoscopic soft robot that can move
safely inside luminal structures. Visual servoing, based on Convolutional
Neural Networks (CNNs) is used to achieve the autonomous navigation task. The
CNN is trained with phantoms and in-vivo data to segment the lumen, and a
model-less approach is presented to control the movement in constrained
environments. The proposed robot is validated in anatomical phantoms in
different path configurations. We analyze the movement of the robot using
different metrics such as task completion time, smoothness, error in the
steady-state, and mean and maximum error. We show that our method is suitable
to navigate safely in hollow environments and conditions which are different
than the ones the network was originally trained on.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 13:17:45 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jul 2022 10:01:28 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Lazo",
"Jorge F.",
""
],
[
"Lai",
"Chun-Feng",
""
],
[
"Moccia",
"Sara",
""
],
[
"Rosa",
"Benoit",
""
],
[
"Catellani",
"Michele",
""
],
[
"de Mathelin",
"Michel",
""
],
[
"Ferrigno",
"Giancarlo",
""
],
[
"Breedveld",
"Paul",
""
],
[
"Dankelman",
"Jenny",
""
],
[
"De Momi",
"Elena",
""
]
] |
new_dataset
| 0.998674 |
2207.03160
|
Zelin Zang
|
Zelin Zang and Siyuan Li and Di Wu and Ge Wang and Lei Shang and
Baigui Sun and Hao Li and Stan Z. Li
|
DLME: Deep Local-flatness Manifold Embedding
|
16 pages, 7 figures
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Manifold learning (ML) aims to seek low-dimensional embedding from
high-dimensional data. The problem is challenging on real-world datasets,
especially with under-sampling data, and we find that previous methods perform
poorly in this case. Generally, ML methods first transform input data into a
low-dimensional embedding space to maintain the data's geometric structure and
subsequently perform downstream tasks therein. The poor local connectivity of
under-sampling data in the former step and inappropriate optimization
objectives in the latter step leads to two problems: structural distortion and
underconstrained embedding. This paper proposes a novel ML framework named Deep
Local-flatness Manifold Embedding (DLME) to solve these problems. The proposed
DLME constructs semantic manifolds by data augmentation and overcomes the
structural distortion problem using a smoothness constrained based on a local
flatness assumption about the manifold. To overcome the underconstrained
embedding problem, we design a loss and theoretically demonstrate that it leads
to a more suitable embedding based on the local flatness. Experiments on three
types of datasets (toy, biological, and image) for various downstream tasks
(classification, clustering, and visualization) show that our proposed DLME
outperforms state-of-the-art ML and contrastive learning methods.
|
[
{
"version": "v1",
"created": "Thu, 7 Jul 2022 08:46:17 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jul 2022 00:47:01 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Zang",
"Zelin",
""
],
[
"Li",
"Siyuan",
""
],
[
"Wu",
"Di",
""
],
[
"Wang",
"Ge",
""
],
[
"Shang",
"Lei",
""
],
[
"Sun",
"Baigui",
""
],
[
"Li",
"Hao",
""
],
[
"Li",
"Stan Z.",
""
]
] |
new_dataset
| 0.999305 |
2207.04429
|
Dhruv Shah
|
Dhruv Shah, Blazej Osinski, Brian Ichter, Sergey Levine
|
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language,
Vision, and Action
|
Project page https://sites.google.com/view/lmnav
| null | null | null |
cs.RO cs.AI cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Goal-conditioned policies for robotic navigation can be trained on large,
unannotated datasets, providing for good generalization to real-world settings.
However, particularly in vision-based settings where specifying goals requires
an image, this makes for an unnatural interface. Language provides a more
convenient modality for communication with robots, but contemporary methods
typically require expensive supervision, in the form of trajectories annotated
with language descriptions. We present a system, LM-Nav, for robotic navigation
that enjoys the benefits of training on unannotated large datasets of
trajectories, while still providing a high-level interface to the user. Instead
of utilizing a labeled instruction following dataset, we show that such a
system can be constructed entirely out of pre-trained models for navigation
(ViNG), image-language association (CLIP), and language modeling (GPT-3),
without requiring any fine-tuning or language-annotated robot data. We
instantiate LM-Nav on a real-world mobile robot and demonstrate long-horizon
navigation through complex, outdoor environments from natural language
instructions. For videos of our experiments, code release, and an interactive
Colab notebook that runs in your browser, please check out our project page
https://sites.google.com/view/lmnav
|
[
{
"version": "v1",
"created": "Sun, 10 Jul 2022 10:41:50 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jul 2022 10:46:15 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Shah",
"Dhruv",
""
],
[
"Osinski",
"Blazej",
""
],
[
"Ichter",
"Brian",
""
],
[
"Levine",
"Sergey",
""
]
] |
new_dataset
| 0.998914 |
2207.06803
|
Yifei He
|
Yifei He, Artur Podobas, M{\aa}ns I. Andersson, and Stefano Markidis
|
FFTc: An MLIR Dialect for Developing HPC Fast Fourier Transform
Libraries
| null | null | null | null |
cs.MS cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Discrete Fourier Transform (DFT) libraries are one of the most critical
software components for scientific computing. Inspired by FFTW, a widely used
library for DFT HPC calculations, we apply compiler technologies for the
development of HPC Fourier transform libraries. In this work, we introduce
FFTc, a domain-specific language, based on Multi-Level Intermediate
Representation (MLIR), for expressing Fourier Transform algorithms. We present
the initial design, implementation, and preliminary results of FFTc.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 10:31:21 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jul 2022 13:48:10 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"He",
"Yifei",
""
],
[
"Podobas",
"Artur",
""
],
[
"Andersson",
"Måns I.",
""
],
[
"Markidis",
"Stefano",
""
]
] |
new_dataset
| 0.998725 |
2207.11617
|
Wei-Sheng Lai
|
Wei-Sheng Lai, YiChang Shih, Lun-Cheng Chu, Xiaotong Wu, Sung-Fang
Tsai, Michael Krainin, Deqing Sun, Chia-Kai Liang
|
Face Deblurring using Dual Camera Fusion on Mobile Phones
|
Accepted to SIGGRAPH 2022 (ACM TOG). Project websit:
https://www.wslai.net/publications/fusion_deblur/
| null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Motion blur of fast-moving subjects is a longstanding problem in photography
and very common on mobile phones due to limited light collection efficiency,
particularly in low-light conditions. While we have witnessed great progress in
image deblurring in recent years, most methods require significant
computational power and have limitations in processing high-resolution photos
with severe local motions. To this end, we develop a novel face deblurring
system based on the dual camera fusion technique for mobile phones. The system
detects subject motion to dynamically enable a reference camera, e.g.,
ultrawide angle camera commonly available on recent premium phones, and
captures an auxiliary photo with faster shutter settings. While the main shot
is low noise but blurry, the reference shot is sharp but noisy. We learn ML
models to align and fuse these two shots and output a clear photo without
motion blur. Our algorithm runs efficiently on Google Pixel 6, which takes 463
ms overhead per shot. Our experiments demonstrate the advantage and robustness
of our system against alternative single-image, multi-frame, face-specific, and
video deblurring algorithms as well as commercial products. To the best of our
knowledge, our work is the first mobile solution for face motion deblurring
that works reliably and robustly over thousands of images in diverse motion and
lighting conditions.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 22:50:46 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Lai",
"Wei-Sheng",
""
],
[
"Shih",
"YiChang",
""
],
[
"Chu",
"Lun-Cheng",
""
],
[
"Wu",
"Xiaotong",
""
],
[
"Tsai",
"Sung-Fang",
""
],
[
"Krainin",
"Michael",
""
],
[
"Sun",
"Deqing",
""
],
[
"Liang",
"Chia-Kai",
""
]
] |
new_dataset
| 0.997712 |
2207.12406
|
Maaz Amjad
|
Maaz Amjad, Grigori Sidorov, Alisa Zhila, Alexander Gelbukh and Paolo
Rosso
|
UrduFake@FIRE2020: Shared Track on Fake News Identification in Urdu
|
arXiv admin note: substantial text overlap with arXiv:2207.11893
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
This paper gives the overview of the first shared task at FIRE 2020 on fake
news detection in the Urdu language. This is a binary classification task in
which the goal is to identify fake news using a dataset composed of 900
annotated news articles for training and 400 news articles for testing. The
dataset contains news in five domains: (i) Health, (ii) Sports, (iii) Showbiz,
(iv) Technology, and (v) Business. 42 teams from 6 different countries (India,
China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams
submitted their experimental results. The participants used various machine
learning methods ranging from feature-based traditional machine learning to
neural network techniques. The best performing system achieved an F-score value
of 0.90, showing that the BERT-based approach outperforms other machine
learning classifiers.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 03:46:51 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Amjad",
"Maaz",
""
],
[
"Sidorov",
"Grigori",
""
],
[
"Zhila",
"Alisa",
""
],
[
"Gelbukh",
"Alexander",
""
],
[
"Rosso",
"Paolo",
""
]
] |
new_dataset
| 0.999891 |
2207.12456
|
Yasharth Bajpai
|
Yuhao Zhang, Yasharth Bajpai, Priyanshu Gupta, Ameya Ketkar, Miltiadis
Allamanis, Titus Barik, Sumit Gulwani, Arjun Radhakrishna, Mohammad Raza,
Gustavo Soares, Ashish Tiwari
|
Overwatch: Learning Patterns in Code Edit Sequences
|
25 pages, 7 Figures, 4 Algorithms, 3 Tables
| null | null | null |
cs.PL cs.AI cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Integrated Development Environments (IDEs) provide tool support to automate
many source code editing tasks. Traditionally, IDEs use only the spatial
context, i.e., the location where the developer is editing, to generate
candidate edit recommendations. However, spatial context alone is often not
sufficient to confidently predict the developer's next edit, and thus IDEs
generate many suggestions at a location. Therefore, IDEs generally do not
actively offer suggestions and instead, the developer is usually required to
click on a specific icon or menu and then select from a large list of potential
suggestions. As a consequence, developers often miss the opportunity to use the
tool support because they are not aware it exists or forget to use it.
To better understand common patterns in developer behavior and produce better
edit recommendations, we can additionally use the temporal context, i.e., the
edits that a developer was recently performing. To enable edit recommendations
based on temporal context, we present Overwatch, a novel technique for learning
edit sequence patterns from traces of developers' edits performed in an IDE.
Our experiments show that Overwatch has 78% precision and that Overwatch not
only completed edits when developers missed the opportunity to use the IDE tool
support but also predicted new edits that have no tool support in the IDE.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 18:24:58 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Zhang",
"Yuhao",
""
],
[
"Bajpai",
"Yasharth",
""
],
[
"Gupta",
"Priyanshu",
""
],
[
"Ketkar",
"Ameya",
""
],
[
"Allamanis",
"Miltiadis",
""
],
[
"Barik",
"Titus",
""
],
[
"Gulwani",
"Sumit",
""
],
[
"Radhakrishna",
"Arjun",
""
],
[
"Raza",
"Mohammad",
""
],
[
"Soares",
"Gustavo",
""
],
[
"Tiwari",
"Ashish",
""
]
] |
new_dataset
| 0.983722 |
2207.12537
|
Sarah Ostadabbas
|
Zhouping Wang and Sarah Ostadabbas
|
Live Stream Temporally Embedded 3D Human Body Pose and Shape Estimation
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
3D Human body pose and shape estimation within a temporal sequence can be
quite critical for understanding human behavior. Despite the significant
progress in human pose estimation in the recent years, which are often based on
single images or videos, human motion estimation on live stream videos is still
a rarely-touched area considering its special requirements for real-time output
and temporal consistency. To address this problem, we present a temporally
embedded 3D human body pose and shape estimation (TePose) method to improve the
accuracy and temporal consistency of pose estimation in live stream videos.
TePose uses previous predictions as a bridge to feedback the error for better
estimation in the current frame and to learn the correspondence between data
frames and predictions in the history. A multi-scale spatio-temporal graph
convolutional network is presented as the motion discriminator for adversarial
training using datasets without any 3D labeling. We propose a sequential data
loading strategy to meet the special start-to-end data processing requirement
of live stream. We demonstrate the importance of each proposed module with
extensive experiments. The results show the effectiveness of TePose on
widely-used human pose benchmarks with state-of-the-art performance.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 21:21:59 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Wang",
"Zhouping",
""
],
[
"Ostadabbas",
"Sarah",
""
]
] |
new_dataset
| 0.96516 |
2207.12544
|
Hongyu Wang
|
Hongyu Wang, Nikolas Martelaro
|
End-User Puppeteering of Expressive Movements
|
Presented at PD/EUP Workshop, 2022 (arXiv:cs/4404636)
| null | null |
PDEUP/2022/05
|
cs.RO cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
The end-user programming of social robot behavior is usually limited by a
predefined set of movements. We are proposing a puppeteering robotic interface
that provides a more intuitive method of programming robot expressive
movements. As the user manipulates the puppet of a robot, the actual robot
replicates the movements, providing real-time visual feedback. Through this
proposed interface, even with limited training, a novice user can design and
program expressive movements efficiently. We present our preliminary user study
results in this extended abstract.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 21:39:06 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Wang",
"Hongyu",
""
],
[
"Martelaro",
"Nikolas",
""
]
] |
new_dataset
| 0.995579 |
2207.12552
|
Vishnu Rajendran S Mr
|
Vishnu Rajendran S, Soran Parsa, Simon Parsons, Amir Ghalamzan
Esfahani
|
Peduncle Gripping and Cutting Force for Strawberry Harvesting Robotic
End-effector Design
|
This work has been submitted to the IEEE for possible publication(4th
International Conference on Control and Robotics (ICCR 2022)). Copyright may
be transferred without notice, after which this version may no longer be
accessible
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Robotic harvesting of strawberries has gained much interest in the recent
past. Although there are many innovations, they haven't yet reached a level
that is comparable to an expert human picker. The end effector unit plays a
major role in defining the efficiency of such a robotic harvesting system. Even
though there are reports on various end effectors for strawberry harvesting,
but there they lack a picture of certain parameters that the researchers can
rely upon to develop new end effectors. These parameters include the limit of
gripping force that can be applied on the peduncle for effective gripping, the
force required to cut the strawberry peduncle, etc. These estimations would be
helpful in the design cycle of the end effectors that target to grip and cut
the strawberry peduncle during the harvesting action. This paper studies the
estimation and analysis of these parameters experimentally. It has been
estimated that the peduncle gripping force can be limited to 10 N. This enables
an end effector to grip a strawberry of mass up to 50 grams with a manipulation
acceleration of 50 m/s$^2$ without squeezing the peduncle. The study on
peduncle cutting force reveals that a force of 15 N is sufficient to cut a
strawberry peduncle using a blade with a wedge angle of 16.6 degrees at a
30-degree orientation.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 22:14:46 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"S",
"Vishnu Rajendran",
""
],
[
"Parsa",
"Soran",
""
],
[
"Parsons",
"Simon",
""
],
[
"Esfahani",
"Amir Ghalamzan",
""
]
] |
new_dataset
| 0.99434 |
2207.12560
|
Pieter Gijsbers
|
Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell,
S\'ebastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren
|
AMLB: an AutoML Benchmark
|
Submitted to JMLR
| null | null | null |
cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
Comparing different AutoML frameworks is notoriously challenging and often
done incorrectly. We introduce an open and extensible benchmark that follows
best practices and avoids common mistakes when comparing AutoML frameworks. We
conduct a thorough comparison of 9 well-known AutoML frameworks across 71
classification and 33 regression tasks. The differences between the AutoML
frameworks are explored with a multi-faceted analysis, evaluating model
accuracy, its trade-offs with inference time, and framework failures. We also
use Bradley-Terry trees to discover subsets of tasks where the relative AutoML
framework rankings differ. The benchmark comes with an open-source tool that
integrates with many AutoML frameworks and automates the empirical evaluation
process end-to-end: from framework installation and resource allocation to
in-depth evaluation. The benchmark uses public data sets, can be easily
extended with other AutoML frameworks and tasks, and has a website with
up-to-date results.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 22:34:08 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Gijsbers",
"Pieter",
""
],
[
"Bueno",
"Marcos L. P.",
""
],
[
"Coors",
"Stefan",
""
],
[
"LeDell",
"Erin",
""
],
[
"Poirier",
"Sébastien",
""
],
[
"Thomas",
"Janek",
""
],
[
"Bischl",
"Bernd",
""
],
[
"Vanschoren",
"Joaquin",
""
]
] |
new_dataset
| 0.978482 |
2207.12572
|
Ruocheng Wang
|
Ruocheng Wang, Yunzhi Zhang, Jiayuan Mao, Chin-Yi Cheng, Jiajun Wu
|
Translating a Visual LEGO Manual to a Machine-Executable Plan
|
ECCV 2022. Project page:
https://cs.stanford.edu/~rcwang/projects/lego_manual
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the problem of translating an image-based, step-by-step assembly
manual created by human designers into machine-interpretable instructions. We
formulate this problem as a sequential prediction task: at each step, our model
reads the manual, locates the components to be added to the current shape, and
infers their 3D poses. This task poses the challenge of establishing a 2D-3D
correspondence between the manual image and the real 3D object, and 3D pose
estimation for unseen 3D objects, since a new component to be added in a step
can be an object built from previous steps. To address these two challenges, we
present a novel learning-based framework, the Manual-to-Executable-Plan Network
(MEPNet), which reconstructs the assembly steps from a sequence of manual
images. The key idea is to integrate neural 2D keypoint detection modules and
2D-3D projection algorithms for high-precision prediction and strong
generalization to unseen components. The MEPNet outperforms existing methods on
three newly collected LEGO manual datasets and a Minecraft house dataset.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 23:35:46 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Wang",
"Ruocheng",
""
],
[
"Zhang",
"Yunzhi",
""
],
[
"Mao",
"Jiayuan",
""
],
[
"Cheng",
"Chin-Yi",
""
],
[
"Wu",
"Jiajun",
""
]
] |
new_dataset
| 0.9997 |
2207.12584
|
Jun Zhang
|
Jun Zhang and Daqing Wan
|
On Deep Holes of Elliptic Curve Codes
|
19 pages
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We give a method to construct deep holes for elliptic curve codes. For long
elliptic curve codes, we conjecture that our construction is complete in the
sense that it gives all deep holes. Some evidence and heuristics on the
completeness are provided via the connection with problems and results in
finite geometry.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 00:29:11 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Zhang",
"Jun",
""
],
[
"Wan",
"Daqing",
""
]
] |
new_dataset
| 0.996625 |
2207.12716
|
Tai Wang
|
Tai Wang, Qing Lian, Chenming Zhu, Xinge Zhu, Wenwei Zhang
|
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained
Monocular Backbones
|
Technical report
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this technical report, we present our solution, dubbed MV-FCOS3D++, for
the Camera-Only 3D Detection track in Waymo Open Dataset Challenge 2022. For
multi-view camera-only 3D detection, methods based on bird-eye-view or 3D
geometric representations can leverage the stereo cues from overlapped regions
between adjacent views and directly perform 3D detection without hand-crafted
post-processing. However, it lacks direct semantic supervision for 2D
backbones, which can be complemented by pretraining simple monocular-based
detectors. Our solution is a multi-view framework for 4D detection following
this paradigm. It is built upon a simple monocular detector FCOS3D++,
pretrained only with object annotations of Waymo, and converts multi-view
features to a 3D grid space to detect 3D objects thereon. A dual-path neck for
single-frame understanding and temporal stereo matching is devised to
incorporate multi-frame information. Our method finally achieves 49.75% mAPL
with a single model and wins 2nd place in the WOD challenge, without any
LiDAR-based depth supervision during training. The code will be released at
https://github.com/Tai-Wang/Depth-from-Motion.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 08:10:29 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Wang",
"Tai",
""
],
[
"Lian",
"Qing",
""
],
[
"Zhu",
"Chenming",
""
],
[
"Zhu",
"Xinge",
""
],
[
"Zhang",
"Wenwei",
""
]
] |
new_dataset
| 0.999753 |
2207.12720
|
Marco Boresta
|
Marco Boresta, Tommaso Colombo, Alberto De Santis
|
Convolutional neural networks and multi-threshold analysis for
contamination detection in the apparel industry
|
23 pages, 8 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Quality control of apparel items is mandatory in modern textile industry, as
consumer's awareness and expectations about the highest possible standard is
constantly increasing in favor of sustainable and ethical textile products.
Such a level of quality is achieved by checking the product throughout its life
cycle, from raw materials to boxed stock. Checks may include color shading
tests, fasteners fatigue tests, fabric weigh tests, contamination tests, etc.
This work deals specifically with the automatic detection of contaminations
given by small parts in the finished product such as raw material like little
stones and plastic bits or materials from the construction process, like a
whole needle or a clip. Identification is performed by a two-level processing
of X-ray images of the items: in the first, a multi-threshold analysis
recognizes the contaminations by gray level and shape attributes; the second
level consists of a deep learning classifier that has been trained to
distinguish between true positives and false positives. The automatic detector
was successfully deployed in an actual production plant, since the results
satisfy the technical specification of the process, namely a number of false
negatives smaller than 3% and a number of false positives smaller than 15%.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 08:21:41 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Boresta",
"Marco",
""
],
[
"Colombo",
"Tommaso",
""
],
[
"De Santis",
"Alberto",
""
]
] |
new_dataset
| 0.970409 |
2207.12730
|
Jiang Bian
|
Jiang Bian, Qingzhong Wang, Haoyi Xiong, Jun Huang, Chen Liu, Xuhong
Li, Jun Cheng, Jun Zhao, Feixiang Lu, Dejing Dou
|
$\textbf{P$^2$A}$: A Dataset and Benchmark for Dense Action Detection
from Table Tennis Match Broadcasting Videos
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While deep learning has been widely used for video analytics, such as video
classification and action detection, dense action detection with fast-moving
subjects from sports videos is still challenging. In this work, we release yet
another sports video dataset $\textbf{P$^2$A}$ for $\underline{P}$ing
$\underline{P}$ong-$\underline{A}$ction detection, which consists of 2,721
video clips collected from the broadcasting videos of professional table tennis
matches in World Table Tennis Championships and Olympiads. We work with a crew
of table tennis professionals and referees to obtain fine-grained action labels
(in 14 classes) for every ping-pong action that appeared in the dataset and
formulate two sets of action detection problems - action localization and
action recognition. We evaluate a number of commonly-seen action recognition
(e.g., TSM, TSN, Video SwinTransformer, and Slowfast) and action localization
models (e.g., BSN, BSN++, BMN, TCANet), using $\textbf{P$^2$A}$ for both
problems, under various settings. These models can only achieve 48% area under
the AR-AN curve for localization and 82% top-one accuracy for recognition since
the ping-pong actions are dense with fast-moving subjects but broadcasting
videos are with only 25 FPS. The results confirm that $\textbf{P$^2$A}$ is
still a challenging task and can be used as a benchmark for action detection
from videos.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 08:34:17 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Bian",
"Jiang",
""
],
[
"Wang",
"Qingzhong",
""
],
[
"Xiong",
"Haoyi",
""
],
[
"Huang",
"Jun",
""
],
[
"Liu",
"Chen",
""
],
[
"Li",
"Xuhong",
""
],
[
"Cheng",
"Jun",
""
],
[
"Zhao",
"Jun",
""
],
[
"Lu",
"Feixiang",
""
],
[
"Dou",
"Dejing",
""
]
] |
new_dataset
| 0.999661 |
2207.12886
|
Mohamed Essam
|
Mohamed Essam, Nagia M. Ghanem and Mohamed A. Ismail
|
Detection of road traffic crashes based on collision estimation
|
11 pages , 9 figures
|
ICDIPV (CS & IT) , 2022
|
10.5121/csit.2022.121213
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces a framework based on computer vision that can detect
road traffic crashes (RCTs) by using the installed surveillance/CCTV camera and
report them to the emergency in real-time with the exact location and time of
occurrence of the accident. The framework is built of five modules. We start
with the detection of vehicles by using YOLO architecture; The second module is
the tracking of vehicles using MOSSE tracker, Then the third module is a new
approach to detect accidents based on collision estimation. Then the fourth
module for each vehicle, we detect if there is a car accident or not based on
the violent flow descriptor (ViF) followed by an SVM classifier for crash
prediction. Finally, in the last stage, if there is a car accident, the system
will send a notification to the emergency by using a GPS module that provides
us with the location, time, and date of the accident to be sent to the
emergency with the help of the GSM module. The main objective is to achieve
higher accuracy with fewer false alarms and to implement a simple system based
on pipelining technique.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 13:21:15 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Essam",
"Mohamed",
""
],
[
"Ghanem",
"Nagia M.",
""
],
[
"Ismail",
"Mohamed A.",
""
]
] |
new_dataset
| 0.986866 |
2207.12909
|
Zerui Chen
|
Zerui Chen, Yana Hasson, Cordelia Schmid, Ivan Laptev
|
AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object
Reconstruction
|
Accepted by ECCV 2022. Project Page:
https://zerchen.github.io/projects/alignsdf.html
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent work achieved impressive progress towards joint reconstruction of
hands and manipulated objects from monocular color images. Existing methods
focus on two alternative representations in terms of either parametric meshes
or signed distance fields (SDFs). On one side, parametric models can benefit
from prior knowledge at the cost of limited shape deformations and mesh
resolutions. Mesh models, hence, may fail to precisely reconstruct details such
as contact surfaces of hands and objects. SDF-based methods, on the other side,
can represent arbitrary details but are lacking explicit priors. In this work
we aim to improve SDF models using priors provided by parametric
representations. In particular, we propose a joint learning framework that
disentangles the pose and the shape. We obtain hand and object poses from
parametric models and use them to align SDFs in 3D space. We show that such
aligned SDFs better focus on reconstructing shape details and improve
reconstruction accuracy both for hands and objects. We evaluate our method and
demonstrate significant improvements over the state of the art on the
challenging ObMan and DexYCB benchmarks.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 13:58:59 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Chen",
"Zerui",
""
],
[
"Hasson",
"Yana",
""
],
[
"Schmid",
"Cordelia",
""
],
[
"Laptev",
"Ivan",
""
]
] |
new_dataset
| 0.999102 |
2207.12935
|
Mingming Fan
|
Xiaofu Jin and Mingming Fan
|
"I Used To Carry A Wallet, Now I Just Need To Carry My Phone":
Understanding Current Banking Practices and Challenges Among Older Adults in
China
|
The 24th International ACM SIGACCESS Conference on Computers and
Accessibility (ASSETS '22)
| null |
10.1145/3517428.3544820
| null |
cs.HC cs.CY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Managing finances is crucial for older adults who are retired and may rely on
savings to ensure their life quality. As digital banking platforms (e.g.,
mobile apps, electronic payment) gradually replace physical ones, it is
critical to understand how they adapt to digital banking and the potential
frictions they experience. We conducted semi-structured interviews with 16
older adults in China, where the aging population is the largest and digital
banking grows fast. We also interviewed bank employees to gain complementary
perspectives of these help givers. Our findings show that older adults used
both physical and digital platforms as an ecosystem based on perceived pros and
cons. Perceived usefulness, self-confidence, and social influence were key
motivators for learning digital banking. They experienced app-related (e.g.,
insufficient error-recovery support) and user-related challenges (e.g., trust,
security and privacy concerns, low perceived self-efficacy) and developed
coping strategies. We discuss design considerations to improve their banking
experiences.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 14:44:09 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Jin",
"Xiaofu",
""
],
[
"Fan",
"Mingming",
""
]
] |
new_dataset
| 0.979197 |
2207.12955
|
Chuhui Xue
|
Chuhui Xue, Jiaxing Huang, Shijian Lu, Changhu Wang, Song Bai
|
Contextual Text Block Detection towards Scene Text Understanding
|
Accepted by ECCV2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most existing scene text detectors focus on detecting characters or words
that only capture partial text messages due to missing contextual information.
For a better understanding of text in scenes, it is more desired to detect
contextual text blocks (CTBs) which consist of one or multiple integral text
units (e.g., characters, words, or phrases) in natural reading order and
transmit certain complete text messages. This paper presents contextual text
detection, a new setup that detects CTBs for better understanding of texts in
scenes. We formulate the new setup by a dual detection task which first detects
integral text units and then groups them into a CTB. To this end, we design a
novel scene text clustering technique that treats integral text units as tokens
and groups them (belonging to the same CTB) into an ordered token sequence. In
addition, we create two datasets SCUT-CTW-Context and ReCTS-Context to
facilitate future research, where each CTB is well annotated by an ordered
sequence of integral text units. Further, we introduce three metrics that
measure contextual text detection in local accuracy, continuity, and global
accuracy. Extensive experiments show that our method accurately detects CTBs
which effectively facilitates downstream tasks such as text classification and
translation. The project is available at
https://sg-vilab.github.io/publication/xue2022contextual/.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 14:59:25 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Xue",
"Chuhui",
""
],
[
"Huang",
"Jiaxing",
""
],
[
"Lu",
"Shijian",
""
],
[
"Wang",
"Changhu",
""
],
[
"Bai",
"Song",
""
]
] |
new_dataset
| 0.99927 |
2207.12966
|
Yijun Yan Dr
|
Yijun Yan, Jinchang Ren, He Sun
|
Nondestructive Quality Control in Powder Metallurgy using Hyperspectral
Imaging
|
8 pages, 9 figures, 3 tables
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Measuring the purity in the metal powder is critical for preserving the
quality of additive manufacturing products. Contamination is one of the most
headache problems which can be caused by multiple reasons and lead to the
as-built components cracking and malfunctions. Existing methods for
metallurgical condition assessment are mostly time-consuming and mainly focus
on the physical integrity of structure rather than material composition.
Through capturing spectral data from a wide frequency range along with the
spatial information, hyperspectral imaging (HSI) can detect minor differences
in terms of temperature, moisture and chemical composition. Therefore, HSI can
provide a unique way to tackle this challenge. In this paper, with the use of a
near-infrared HSI camera, applications of HSI for the non-destructive
inspection of metal powders are introduced. Technical assumptions and solutions
on three step-by-step case studies are presented in detail, including powder
characterization, contamination detection, and band selection analysis.
Experimental results have fully demonstrated the great potential of HSI and
related AI techniques for NDT of powder metallurgy, especially the potential to
satisfy the industrial manufacturing environment.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 15:20:35 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Yan",
"Yijun",
""
],
[
"Ren",
"Jinchang",
""
],
[
"Sun",
"He",
""
]
] |
new_dataset
| 0.992652 |
2207.13005
|
Zhenran Xu
|
Zhenran Xu, Zifei Shan, Yuxin Li, Baotian Hu, Bing Qin
|
Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark
|
19 pages, 3 figures, 12 tables. Dataset available at
https://github.com/imryanxu/Hansel
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Modern Entity Linking (EL) systems entrench a popularity bias, yet there is
no dataset focusing on tail and emerging entities in languages other than
English. We present Hansel, a new benchmark in Chinese that fills the vacancy
of non-English few-shot and zero-shot EL challenges. The test set of Hansel is
human annotated and reviewed, created with a novel method for collecting
zero-shot EL datasets. It covers 10K diverse documents in news, social media
posts and other web articles, with Wikidata as its target Knowledge Base. We
demonstrate that the existing state-of-the-art EL system performs poorly on
Hansel (R@1 of 36.6% on Few-Shot). We then establish a strong baseline that
scores a R@1 of 46.2% on Few-Shot and 76.6% on Zero-Shot on our dataset. We
also show that our baseline achieves competitive results on TAC-KBP2015 Chinese
Entity Linking task.
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 16:09:07 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Xu",
"Zhenran",
""
],
[
"Shan",
"Zifei",
""
],
[
"Li",
"Yuxin",
""
],
[
"Hu",
"Baotian",
""
],
[
"Qin",
"Bing",
""
]
] |
new_dataset
| 0.996789 |
2207.13075
|
Tarik A. Rashid
|
Maryam T. Abdulkhaleq, Tarik A. Rashid, Abeer Alsadoon, Bryar A.
Hassan, Mokhtar Mohammadi, Jaza M. Abdullah, Amit Chhabra, Sazan L. Ali,
Rawshan N. Othman, Hadil A. Hasan, Sara Azad, Naz A. Mahmood, Sivan S.
Abdalrahman, Hezha O. Rasul, Nebojsa Bacanin, S.Vimal
|
Harmony Search: Current Studies and Uses on Healthcare Systems
|
37 pages
|
Artificial Intelligence in Medicine, 2022
|
10.1016/j.artmed.2022.102348
| null |
cs.NE cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
One of the popular metaheuristic search algorithms is Harmony Search (HS). It
has been verified that HS can find solutions to optimization problems due to
its balanced exploratory and convergence behavior and its simple and flexible
structure. This capability makes the algorithm preferable to be applied in
several real-world applications in various fields, including healthcare
systems, different engineering fields, and computer science. The popularity of
HS urges us to provide a comprehensive survey of the literature on HS and its
variants on health systems, analyze its strengths and weaknesses, and suggest
future research directions. In this review paper, the current studies and uses
of harmony search are studied in four main domains. (i) The variants of HS,
including its modifications and hybridization. (ii) Summary of the previous
review works. (iii) Applications of HS in healthcare systems. (iv) And finally,
an operational framework is proposed for the applications of HS in healthcare
systems. The main contribution of this review is intended to provide a thorough
examination of HS in healthcare systems while also serving as a valuable
resource for prospective scholars who want to investigate or implement this
method.
|
[
{
"version": "v1",
"created": "Tue, 19 Jul 2022 08:37:42 GMT"
}
] | 2022-07-27T00:00:00 |
[
[
"Abdulkhaleq",
"Maryam T.",
""
],
[
"Rashid",
"Tarik A.",
""
],
[
"Alsadoon",
"Abeer",
""
],
[
"Hassan",
"Bryar A.",
""
],
[
"Mohammadi",
"Mokhtar",
""
],
[
"Abdullah",
"Jaza M.",
""
],
[
"Chhabra",
"Amit",
""
],
[
"Ali",
"Sazan L.",
""
],
[
"Othman",
"Rawshan N.",
""
],
[
"Hasan",
"Hadil A.",
""
],
[
"Azad",
"Sara",
""
],
[
"Mahmood",
"Naz A.",
""
],
[
"Abdalrahman",
"Sivan S.",
""
],
[
"Rasul",
"Hezha O.",
""
],
[
"Bacanin",
"Nebojsa",
""
],
[
"Vimal",
"S.",
""
]
] |
new_dataset
| 0.957146 |
2104.09035
|
Liang Peng
|
Liang Peng, Fei Liu, Zhengxu Yu, Senbo Yan, Dan Deng, Zheng Yang,
Haifeng Liu, Deng Cai
|
Lidar Point Cloud Guided Monocular 3D Object Detection
|
ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Monocular 3D object detection is a challenging task in the self-driving and
computer vision community. As a common practice, most previous works use
manually annotated 3D box labels, where the annotating process is expensive. In
this paper, we find that the precisely and carefully annotated labels may be
unnecessary in monocular 3D detection, which is an interesting and
counterintuitive finding. Using rough labels that are randomly disturbed, the
detector can achieve very close accuracy compared to the one using the
ground-truth labels. We delve into this underlying mechanism and then
empirically find that: concerning the label accuracy, the 3D location part in
the label is preferred compared to other parts of labels. Motivated by the
conclusions above and considering the precise LiDAR 3D measurement, we propose
a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D
object detection (LPCG). This framework is capable of either reducing the
annotation costs or considerably boosting the detection accuracy without
introducing extra annotation costs. Specifically, It generates pseudo labels
from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in
3D space, such pseudo labels can replace manually annotated labels in the
training of monocular 3D detectors, since their 3D location information is
precise. LPCG can be applied into any monocular 3D detector to fully use
massive unlabeled data in a self-driving system. As a result, in KITTI
benchmark, we take the first place on both monocular 3D and BEV
(bird's-eye-view) detection with a significant margin. In Waymo benchmark, our
method using 10% labeled data achieves comparable accuracy to the baseline
detector using 100% labeled data. The codes are released at
https://github.com/SPengLiang/LPCG.
|
[
{
"version": "v1",
"created": "Mon, 19 Apr 2021 03:41:09 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Sep 2021 12:07:50 GMT"
},
{
"version": "v3",
"created": "Mon, 25 Jul 2022 03:11:46 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Peng",
"Liang",
""
],
[
"Liu",
"Fei",
""
],
[
"Yu",
"Zhengxu",
""
],
[
"Yan",
"Senbo",
""
],
[
"Deng",
"Dan",
""
],
[
"Yang",
"Zheng",
""
],
[
"Liu",
"Haifeng",
""
],
[
"Cai",
"Deng",
""
]
] |
new_dataset
| 0.951166 |
2106.00515
|
Pichao Wang
|
Pichao Wang and Xue Wang and Fan Wang and Ming Lin and Shuning Chang
and Hao Li and Rong Jin
|
KVT: k-NN Attention for Boosting Vision Transformers
|
Accepted by ECCV 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Convolutional Neural Networks (CNNs) have dominated computer vision for
years, due to its ability in capturing locality and translation invariance.
Recently, many vision transformer architectures have been proposed and they
show promising performance. A key component in vision transformers is the
fully-connected self-attention which is more powerful than CNNs in modelling
long range dependencies. However, since the current dense self-attention uses
all image patches (tokens) to compute attention matrix, it may neglect locality
of images patches and involve noisy tokens (e.g., clutter background and
occlusion), leading to a slow training process and potential degradation of
performance. To address these problems, we propose the $k$-NN attention for
boosting vision transformers. Specifically, instead of involving all the tokens
for attention matrix calculation, we only select the top-$k$ similar tokens
from the keys for each query to compute the attention map. The proposed $k$-NN
attention naturally inherits the local bias of CNNs without introducing
convolutional operations, as nearby tokens tend to be more similar than others.
In addition, the $k$-NN attention allows for the exploration of long range
correlation and at the same time filters out irrelevant tokens by choosing the
most similar tokens from the entire image. Despite its simplicity, we verify,
both theoretically and empirically, that $k$-NN attention is powerful in
speeding up training and distilling noise from input tokens. Extensive
experiments are conducted by using 11 different vision transformer
architectures to verify that the proposed $k$-NN attention can work with any
existing transformer architectures to improve its prediction performance. The
codes are available at \url{https://github.com/damo-cv/KVT}.
|
[
{
"version": "v1",
"created": "Fri, 28 May 2021 06:49:10 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jan 2022 00:53:35 GMT"
},
{
"version": "v3",
"created": "Fri, 22 Jul 2022 23:18:16 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Wang",
"Pichao",
""
],
[
"Wang",
"Xue",
""
],
[
"Wang",
"Fan",
""
],
[
"Lin",
"Ming",
""
],
[
"Chang",
"Shuning",
""
],
[
"Li",
"Hao",
""
],
[
"Jin",
"Rong",
""
]
] |
new_dataset
| 0.999185 |
2106.11239
|
Dan Jia
|
Dan Jia and Alexander Hermans and Bastian Leibe
|
2D vs. 3D LiDAR-based Person Detection on Mobile Robots
|
Shortened version accepted at the International Conference on
Intelligent Robots and Systems (IROS) 2022
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Person detection is a crucial task for mobile robots navigating in
human-populated environments. LiDAR sensors are promising for this task, thanks
to their accurate depth measurements and large field of view. Two types of
LiDAR sensors exist: the 2D LiDAR sensors, which scan a single plane, and the
3D LiDAR sensors, which scan multiple planes, thus forming a volume. How do
they compare for the task of person detection? To answer this, we conduct a
series of experiments, using the public, large-scale JackRabbot dataset and the
state-of-the-art 2D and 3D LiDAR-based person detectors (DR-SPAAM and
CenterPoint respectively). Our experiments include multiple aspects, ranging
from the basic performance and speed comparison, to more detailed analysis on
localization accuracy and robustness against distance and scene clutter. The
insights from these experiments highlight the strengths and weaknesses of 2D
and 3D LiDAR sensors as sources for person detection, and are especially
valuable for designing mobile robots that will operate in close proximity to
surrounding humans (e.g. service or social robot).
|
[
{
"version": "v1",
"created": "Mon, 21 Jun 2021 16:35:49 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2022 12:27:30 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Jia",
"Dan",
""
],
[
"Hermans",
"Alexander",
""
],
[
"Leibe",
"Bastian",
""
]
] |
new_dataset
| 0.998239 |
2107.13824
|
Zeyu Hu
|
Zeyu Hu, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin
Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai
|
VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation
|
V1: ICCV2021(Oral), supplementary materials included V2:
TPAMI(ICCV2021 SI), supplementary materials included
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, sparse voxel-based methods have become the state-of-the-arts
for 3D semantic segmentation of indoor scenes, thanks to the powerful 3D CNNs.
Nevertheless, being oblivious to the underlying geometry, voxel-based methods
suffer from ambiguous features on spatially close objects and struggle with
handling complex and irregular geometries due to the lack of geodesic
information. In view of this, we present Voxel-Mesh Network (VMNet), a novel 3D
deep architecture that operates on the voxel and mesh representations
leveraging both the Euclidean and geodesic information. Intuitively, the
Euclidean information extracted from voxels can offer contextual cues
representing interactions between nearby objects, while the geodesic
information extracted from meshes can help separate objects that are spatially
close but have disconnected surfaces. To incorporate such information from the
two domains, we design an intra-domain attentive module for effective feature
aggregation and an inter-domain attentive module for adaptive feature fusion.
Experimental results validate the effectiveness of VMNet: specifically, on the
challenging ScanNet dataset for large-scale segmentation of indoor scenes, it
outperforms the state-of-the-art SparseConvNet and MinkowskiNet (74.6% vs 72.5%
and 73.6% in mIoU) with a simpler network structure (17M vs 30M and 38M
parameters). Code release: https://github.com/hzykent/VMNet
|
[
{
"version": "v1",
"created": "Thu, 29 Jul 2021 08:41:14 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2022 06:58:20 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Hu",
"Zeyu",
""
],
[
"Bai",
"Xuyang",
""
],
[
"Shang",
"Jiaxiang",
""
],
[
"Zhang",
"Runze",
""
],
[
"Dong",
"Jiayu",
""
],
[
"Wang",
"Xin",
""
],
[
"Sun",
"Guangyuan",
""
],
[
"Fu",
"Hongbo",
""
],
[
"Tai",
"Chiew-Lan",
""
]
] |
new_dataset
| 0.97296 |
2108.01806
|
Binh-Son Hua
|
Hong-Wing Pang, Yingshu Chen, Phuoc-Hieu Le, Binh-Son Hua, Duc Thanh
Nguyen, Sai-Kit Yeung
|
Neural Scene Decoration from a Single Photograph
|
ECCV 2022 paper. 14 pages of main content, 4 pages of references, and
11 pages of appendix
| null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Furnishing and rendering indoor scenes has been a long-standing task for
interior design, where artists create a conceptual design for the space, build
a 3D model of the space, decorate, and then perform rendering. Although the
task is important, it is tedious and requires tremendous effort. In this paper,
we introduce a new problem of domain-specific indoor scene image synthesis,
namely neural scene decoration. Given a photograph of an empty indoor space and
a list of decorations with layout determined by user, we aim to synthesize a
new image of the same space with desired furnishing and decorations. Neural
scene decoration can be applied to create conceptual interior designs in a
simple yet effective manner. Our attempt to this research problem is a novel
scene generation architecture that transforms an empty scene and an object
layout into a realistic furnished scene photograph. We demonstrate the
performance of our proposed method by comparing it with conditional image
synthesis baselines built upon prevailing image translation approaches both
qualitatively and quantitatively. We conduct extensive experiments to further
validate the plausibility and aesthetics of our generated scenes. Our
implementation is available at
\url{https://github.com/hkust-vgd/neural_scene_decoration}.
|
[
{
"version": "v1",
"created": "Wed, 4 Aug 2021 01:44:21 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2022 14:11:37 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Pang",
"Hong-Wing",
""
],
[
"Chen",
"Yingshu",
""
],
[
"Le",
"Phuoc-Hieu",
""
],
[
"Hua",
"Binh-Son",
""
],
[
"Nguyen",
"Duc Thanh",
""
],
[
"Yeung",
"Sai-Kit",
""
]
] |
new_dataset
| 0.998661 |
2108.12144
|
Qingyuan Liang
|
Qingyuan Liang, Zeyu Sun, Qihao Zhu, Wenjie Zhang, Lian Yu, Yingfei
Xiong, Lu Zhang
|
Lyra: A Benchmark for Turducken-Style Code Generation
| null | null | null | null |
cs.SE cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Recently, neural techniques have been used to generate source code
automatically. While promising for declarative languages, these approaches
achieve much poorer performance on datasets for imperative languages. Since a
declarative language is typically embedded in an imperative language (i.e., the
turducken-style programming) in real-world software development, the promising
results on declarative languages can hardly lead to significant reduction of
manual software development efforts. In this paper, we define a new code
generation task: given a natural language comment, this task aims to generate a
program in a base imperative language with an embedded declarative language. To
our knowledge, this is the first turducken-style code generation task. For this
task, we present Lyra: a dataset in Python with embedded SQL. This dataset
contains 2,000 carefully annotated database manipulation programs from
real-world projects. Each program is paired with both a Chinese comment and an
English comment. In our experiment, we adopted Transformer, BERT-style, and
GPT-style models as baselines. In the best setting, the generation performance
of GPT-style models is better than others, where the AST exact matching
accuracy is 24% and 25.5% when using Chinese and English comments,
respectively. Therefore, we believe that Lyra provides a new challenge for code
generation. Yet, overcoming this challenge may significantly boost the
applicability of code generation techniques for real-world software
development.
|
[
{
"version": "v1",
"created": "Fri, 27 Aug 2021 07:22:55 GMT"
},
{
"version": "v2",
"created": "Wed, 4 May 2022 15:59:44 GMT"
},
{
"version": "v3",
"created": "Sun, 24 Jul 2022 04:54:17 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Liang",
"Qingyuan",
""
],
[
"Sun",
"Zeyu",
""
],
[
"Zhu",
"Qihao",
""
],
[
"Zhang",
"Wenjie",
""
],
[
"Yu",
"Lian",
""
],
[
"Xiong",
"Yingfei",
""
],
[
"Zhang",
"Lu",
""
]
] |
new_dataset
| 0.999652 |
2110.07718
|
Yunxiao Qin
|
Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Lihong Cao, Cho-Jui Hsieh
|
Adversarial Attack across Datasets
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Existing transfer attack methods commonly assume that the attacker knows the
training set (e.g., the label set, the input size) of the black-box victim
models, which is usually unrealistic because in some cases the attacker cannot
know this information. In this paper, we define a Generalized Transferable
Attack (GTA) problem where the attacker doesn't know this information and is
acquired to attack any randomly encountered images that may come from unknown
datasets. To solve the GTA problem, we propose a novel Image Classification
Eraser (ICE) that trains a particular attacker to erase classification
information of any images from arbitrary datasets. Experiments on several
datasets demonstrate that ICE greatly outperforms existing transfer attacks on
GTA, and show that ICE uses similar texture-like noises to perturb different
images from different datasets. Moreover, fast fourier transformation analysis
indicates that the main components in each ICE noise are three sine waves for
the R, G, and B image channels. Inspired by this interesting finding, we then
design a novel Sine Attack (SA) method to optimize the three sine waves.
Experiments show that SA performs comparably to ICE, indicating that the three
sine waves are effective and enough to break DNNs under the GTA setting.
|
[
{
"version": "v1",
"created": "Wed, 13 Oct 2021 02:07:40 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2022 14:21:12 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Qin",
"Yunxiao",
""
],
[
"Xiong",
"Yuanhao",
""
],
[
"Yi",
"Jinfeng",
""
],
[
"Cao",
"Lihong",
""
],
[
"Hsieh",
"Cho-Jui",
""
]
] |
new_dataset
| 0.998717 |
2110.09004
|
Diwei Sheng
|
Diwei Sheng, Yuxiang Chai, Xinru Li, Chen Feng, Jianzhe Lin, Claudio
Silva, John-Ross Rizzo
|
NYU-VPR: Long-Term Visual Place Recognition Benchmark with View
Direction and Data Anonymization Influences
|
8 pages, 10 figures, published in 2021 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2021)
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Visual place recognition (VPR) is critical in not only localization and
mapping for autonomous driving vehicles, but also in assistive navigation for
the visually impaired population. To enable a long-term VPR system on a large
scale, several challenges need to be addressed. First, different applications
could require different image view directions, such as front views for
self-driving cars while side views for the low vision people. Second, VPR in
metropolitan scenes can often cause privacy concerns due to the imaging of
pedestrian and vehicle identity information, calling for the need for data
anonymization before VPR queries and database construction. Both factors could
lead to VPR performance variations that are not well understood yet. To study
their influences, we present the NYU-VPR dataset that contains more than
200,000 images over a 2km by 2km area near the New York University campus,
taken within the whole year of 2016. We present benchmark results on several
popular VPR algorithms showing that side views are significantly more
challenging for current VPR methods while the influence of data anonymization
is almost negligible, together with our hypothetical explanations and in-depth
analysis.
|
[
{
"version": "v1",
"created": "Mon, 18 Oct 2021 03:56:33 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2022 05:43:04 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Sheng",
"Diwei",
""
],
[
"Chai",
"Yuxiang",
""
],
[
"Li",
"Xinru",
""
],
[
"Feng",
"Chen",
""
],
[
"Lin",
"Jianzhe",
""
],
[
"Silva",
"Claudio",
""
],
[
"Rizzo",
"John-Ross",
""
]
] |
new_dataset
| 0.998923 |
2110.13214
|
Pan Lu
|
Pan Lu, Liang Qiu, Jiaqi Chen, Tony Xia, Yizhou Zhao, Wei Zhang, Zhou
Yu, Xiaodan Liang, Song-Chun Zhu
|
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual
Language Reasoning
|
Corrected typos. Accepted to NeurIPS 2021, 27 pages, 18 figures. Data
and code are available at https://iconqa.github.io
| null | null | null |
cs.CV cs.AI cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Current visual question answering (VQA) tasks mainly consider answering
human-annotated questions for natural images. However, aside from natural
images, abstract diagrams with semantic richness are still understudied in
visual understanding and reasoning research. In this work, we introduce a new
challenge of Icon Question Answering (IconQA) with the goal of answering a
question in an icon image context. We release IconQA, a large-scale dataset
that consists of 107,439 questions and three sub-tasks: multi-image-choice,
multi-text-choice, and filling-in-the-blank. The IconQA dataset is inspired by
real-world diagram word problems that highlight the importance of abstract
diagram understanding and comprehensive cognitive reasoning. Thus, IconQA
requires not only perception skills like object recognition and text
understanding, but also diverse cognitive reasoning skills, such as geometric
reasoning, commonsense reasoning, and arithmetic reasoning. To facilitate
potential IconQA models to learn semantic representations for icon images, we
further release an icon dataset Icon645 which contains 645,687 colored icons on
377 classes. We conduct extensive user studies and blind experiments and
reproduce a wide range of advanced VQA methods to benchmark the IconQA task.
Also, we develop a strong IconQA baseline Patch-TRM that applies a pyramid
cross-modal Transformer with input diagram embeddings pre-trained on the icon
dataset. IconQA and Icon645 are available at https://iconqa.github.io.
|
[
{
"version": "v1",
"created": "Mon, 25 Oct 2021 18:52:26 GMT"
},
{
"version": "v2",
"created": "Sun, 7 Nov 2021 00:44:53 GMT"
},
{
"version": "v3",
"created": "Sun, 20 Feb 2022 01:09:40 GMT"
},
{
"version": "v4",
"created": "Mon, 25 Jul 2022 04:05:29 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Lu",
"Pan",
""
],
[
"Qiu",
"Liang",
""
],
[
"Chen",
"Jiaqi",
""
],
[
"Xia",
"Tony",
""
],
[
"Zhao",
"Yizhou",
""
],
[
"Zhang",
"Wei",
""
],
[
"Yu",
"Zhou",
""
],
[
"Liang",
"Xiaodan",
""
],
[
"Zhu",
"Song-Chun",
""
]
] |
new_dataset
| 0.999858 |
2112.05892
|
Honglu Zhou
|
Honglu Zhou, Asim Kadav, Aviv Shamsian, Shijie Geng, Farley Lai, Long
Zhao, Ting Liu, Mubbasir Kapadia, Hans Peter Graf
|
COMPOSER: Compositional Reasoning of Group Activity in Videos with
Keypoint-Only Modality
|
ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Group Activity Recognition detects the activity collectively performed by a
group of actors, which requires compositional reasoning of actors and objects.
We approach the task by modeling the video as tokens that represent the
multi-scale semantic concepts in the video. We propose COMPOSER, a Multiscale
Transformer based architecture that performs attention-based reasoning over
tokens at each scale and learns group activity compositionally. In addition,
prior works suffer from scene biases with privacy and ethical concerns. We only
use the keypoint modality which reduces scene biases and prevents acquiring
detailed visual data that may contain private or biased information of users.
We improve the multiscale representations in COMPOSER by clustering the
intermediate scale representations, while maintaining consistent cluster
assignments between scales. Finally, we use techniques such as auxiliary
prediction and data augmentations tailored to the keypoint signals to aid model
training. We demonstrate the model's strength and interpretability on two
widely-used datasets (Volleyball and Collective Activity). COMPOSER achieves up
to +5.4% improvement with just the keypoint modality. Code is available at
https://github.com/hongluzhou/composer
|
[
{
"version": "v1",
"created": "Sat, 11 Dec 2021 01:25:46 GMT"
},
{
"version": "v2",
"created": "Sun, 20 Mar 2022 03:35:16 GMT"
},
{
"version": "v3",
"created": "Mon, 25 Jul 2022 00:38:32 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Zhou",
"Honglu",
""
],
[
"Kadav",
"Asim",
""
],
[
"Shamsian",
"Aviv",
""
],
[
"Geng",
"Shijie",
""
],
[
"Lai",
"Farley",
""
],
[
"Zhao",
"Long",
""
],
[
"Liu",
"Ting",
""
],
[
"Kapadia",
"Mubbasir",
""
],
[
"Graf",
"Hans Peter",
""
]
] |
new_dataset
| 0.998089 |
2201.05961
|
Hanjia Lyu
|
Arsal Imtiaz, Danish Khan, Hanjia Lyu, Jiebo Luo
|
Taking sides: Public Opinion over the Israel-Palestine Conflict in 2021
|
Accepted for publication in Proceedings of the International Workshop
on Social Sensing (SocialSens 2022): Special Edition on Belief Dynamics, 2022
| null | null | null |
cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Israel-Palestine Conflict, one of the most enduring conflicts in history,
dates back to the start of 20th century, with the establishment of the British
Mandate in Palestine and has deeply rooted complex issues in politics,
demography, religion, and other aspects, making it harder to attain resolve. To
understand the conflict in 2021, we devise an observational study to aggregate
stance held by English-speaking countries. We collect Twitter data using
popular hashtags around and specific to the conflict portraying opinions
neutral or partial to the two parties. We use different tools and methods to
classify tweets into pro-Palestinian, pro-Israel, or neutral. This paper
further describes the implementation of data mining methodologies to obtain
insights and reason the stance held by citizens around the conflict.
|
[
{
"version": "v1",
"created": "Sun, 16 Jan 2022 04:03:36 GMT"
},
{
"version": "v2",
"created": "Sat, 23 Jul 2022 23:56:36 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Imtiaz",
"Arsal",
""
],
[
"Khan",
"Danish",
""
],
[
"Lyu",
"Hanjia",
""
],
[
"Luo",
"Jiebo",
""
]
] |
new_dataset
| 0.997858 |
2202.04800
|
Jack Hessel
|
Jack Hessel and Jena D. Hwang and Jae Sung Park and Rowan Zellers and
Chandra Bhagavatula and Anna Rohrbach and Kate Saenko and Yejin Choi
|
The Abduction of Sherlock Holmes: A Dataset for Visual Abductive
Reasoning
|
code, data, models at http://visualabduction.com/
|
ECCV 2022
| null | null |
cs.CV cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Humans have remarkable capacity to reason abductively and hypothesize about
what lies beyond the literal content of an image. By identifying concrete
visual clues scattered throughout a scene, we almost can't help but draw
probable inferences beyond the literal scene based on our everyday experience
and knowledge about the world. For example, if we see a "20 mph" sign alongside
a road, we might assume the street sits in a residential area (rather than on a
highway), even if no houses are pictured. Can machines perform similar visual
reasoning?
We present Sherlock, an annotated corpus of 103K images for testing machine
capacity for abductive reasoning beyond literal image contents. We adopt a
free-viewing paradigm: participants first observe and identify salient clues
within images (e.g., objects, actions) and then provide a plausible inference
about the scene, given the clue. In total, we collect 363K (clue, inference)
pairs, which form a first-of-its-kind abductive visual reasoning dataset. Using
our corpus, we test three complementary axes of abductive reasoning. We
evaluate the capacity of models to: i) retrieve relevant inferences from a
large candidate corpus; ii) localize evidence for inferences via bounding
boxes, and iii) compare plausible inferences to match human judgments on a
newly-collected diagnostic corpus of 19K Likert-scale judgments. While we find
that fine-tuning CLIP-RN50x64 with a multitask objective outperforms strong
baselines, significant headroom exists between model performance and human
agreement. Data, models, and leaderboard available at
http://visualabduction.com/
|
[
{
"version": "v1",
"created": "Thu, 10 Feb 2022 02:26:45 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2022 17:26:06 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Hessel",
"Jack",
""
],
[
"Hwang",
"Jena D.",
""
],
[
"Park",
"Jae Sung",
""
],
[
"Zellers",
"Rowan",
""
],
[
"Bhagavatula",
"Chandra",
""
],
[
"Rohrbach",
"Anna",
""
],
[
"Saenko",
"Kate",
""
],
[
"Choi",
"Yejin",
""
]
] |
new_dataset
| 0.999796 |
2202.10448
|
Deepak Pathak
|
Aravind Sivakumar, Kenneth Shaw, Deepak Pathak
|
Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans
on Youtube
|
RSS 2022 final version. Website and demos at
https://robotic-telekinesis.github.io/
| null | null | null |
cs.RO cs.AI cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We build a system that enables any human to control a robot hand and arm,
simply by demonstrating motions with their own hand. The robot observes the
human operator via a single RGB camera and imitates their actions in real-time.
Human hands and robot hands differ in shape, size, and joint structure, and
performing this translation from a single uncalibrated camera is a highly
underconstrained problem. Moreover, the retargeted trajectories must
effectively execute tasks on a physical robot, which requires them to be
temporally smooth and free of self-collisions. Our key insight is that while
paired human-robot correspondence data is expensive to collect, the internet
contains a massive corpus of rich and diverse human hand videos. We leverage
this data to train a system that understands human hands and retargets a human
video stream into a robot hand-arm trajectory that is smooth, swift, safe, and
semantically similar to the guiding demonstration. We demonstrate that it
enables previously untrained people to teleoperate a robot on various dexterous
manipulation tasks. Our low-cost, glove-free, marker-free remote teleoperation
system makes robot teaching more accessible and we hope that it can aid robots
in learning to act autonomously in the real world. Videos at
https://robotic-telekinesis.github.io/
|
[
{
"version": "v1",
"created": "Mon, 21 Feb 2022 18:59:59 GMT"
},
{
"version": "v2",
"created": "Sun, 24 Jul 2022 06:08:35 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Sivakumar",
"Aravind",
""
],
[
"Shaw",
"Kenneth",
""
],
[
"Pathak",
"Deepak",
""
]
] |
new_dataset
| 0.999193 |
2203.02118
|
Ruixiang Cao
|
Ruixiang Cao, Jun Gu, Chen Yu and Andre Rosendo
|
OmniWheg: An Omnidirectional Wheel-Leg Transformable Robot
|
6 pages, 10 figures, IROS
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents the design, analysis, and performance evaluation of an
omnidirectional transformable wheel-leg robot called OmniWheg. We design a
novel mechanism consisting of a separable omni-wheel and 4-bar linkages,
allowing the robot to transform between omni-wheeled and legged modes smoothly.
In wheeled mode, the robot can move in all directions and efficiently adjust
the relative position of its wheels, while it can overcome common obstacles in
legged mode, such as stairs and steps. Unlike other articles studying whegs,
this implementation with omnidirectional wheels allows the correction of
misalignments between right and left wheels before traversing obstacles, which
effectively improves the success rate and simplifies the preparation process
before the wheel-leg transformation. We describe the design concept, mechanism,
and the dynamic characteristic of the wheel-leg structure. We then evaluate its
performance in various scenarios, including passing obstacles, climbing steps
of different heights, and turning/moving omnidirectionally. Our results confirm
that this mobile platform can overcome common indoor obstacles and move
flexibly on the flat ground with the new transformable wheel-leg mechanism,
while keeping a high degree of stability.
|
[
{
"version": "v1",
"created": "Fri, 4 Mar 2022 03:23:02 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2022 13:16:46 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Cao",
"Ruixiang",
""
],
[
"Gu",
"Jun",
""
],
[
"Yu",
"Chen",
""
],
[
"Rosendo",
"Andre",
""
]
] |
new_dataset
| 0.97217 |
2204.05483
|
Stefan Larson
|
Stefan Larson, Kevin Leach
|
Redwood: Using Collision Detection to Grow a Large-Scale Intent
Classification Dataset
|
SIGDIAL 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Dialog systems must be capable of incorporating new skills via updates over
time in order to reflect new use cases or deployment scenarios. Similarly,
developers of such ML-driven systems need to be able to add new training data
to an already-existing dataset to support these new skills. In intent
classification systems, problems can arise if training data for a new skill's
intent overlaps semantically with an already-existing intent. We call such
cases collisions. This paper introduces the task of intent collision detection
between multiple datasets for the purposes of growing a system's skillset. We
introduce several methods for detecting collisions, and evaluate our methods on
real datasets that exhibit collisions. To highlight the need for intent
collision detection, we show that model performance suffers if new data is
added in such a way that does not arbitrate colliding intents. Finally, we use
collision detection to construct and benchmark a new dataset, Redwood, which is
composed of 451 ntent categories from 13 original intent classification
datasets, making it the largest publicly available intent classification
benchmark.
|
[
{
"version": "v1",
"created": "Tue, 12 Apr 2022 02:28:23 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2022 16:57:42 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Larson",
"Stefan",
""
],
[
"Leach",
"Kevin",
""
]
] |
new_dataset
| 0.998129 |
2205.02301
|
Dorian F. Henning
|
Dorian F. Henning, Tristan Laidlow, Stefan Leutenegger
|
BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking
|
ECCV 2022. Video: https://youtu.be/0-SL3VeWEvU
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Estimating human motion from video is an active research area due to its many
potential applications. Most state-of-the-art methods predict human shape and
posture estimates for individual images and do not leverage the temporal
information available in video. Many "in the wild" sequences of human motion
are captured by a moving camera, which adds the complication of conflated
camera and human motion to the estimation. We therefore present BodySLAM, a
monocular SLAM system that jointly estimates the position, shape, and posture
of human bodies, as well as the camera trajectory. We also introduce a novel
human motion model to constrain sequential body postures and observe the scale
of the scene. Through a series of experiments on video sequences of human
motion captured by a moving monocular camera, we demonstrate that BodySLAM
improves estimates of all human body parameters and camera poses when compared
to estimating these separately.
|
[
{
"version": "v1",
"created": "Wed, 4 May 2022 19:38:26 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 13:26:30 GMT"
},
{
"version": "v3",
"created": "Sun, 24 Jul 2022 20:52:48 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Henning",
"Dorian F.",
""
],
[
"Laidlow",
"Tristan",
""
],
[
"Leutenegger",
"Stefan",
""
]
] |
new_dataset
| 0.998188 |
2205.02428
|
Guanzhou Li
|
Guanzhou Li, Jianping Wu, Yujing He
|
HARL: A Novel Hierachical Adversary Reinforcement Learning for
Automoumous Intersection Management
| null | null | null | null |
cs.MA cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As an emerging technology, Connected Autonomous Vehicles (CAVs) are believed
to have the ability to move through intersections in a faster and safer manner,
through effective Vehicle-to-Everything (V2X) communication and global
observation. Autonomous intersection management is a key path to efficient
crossing at intersections, which reduces unnecessary slowdowns and stops
through adaptive decision process of each CAV, enabling fuller utilization of
the intersection space. Distributed reinforcement learning (DRL) offers a
flexible, end-to-end model for AIM, adapting for many intersection scenarios.
While DRL is prone to collisions as the actions of multiple sides in the
complicated interactions are sampled from a generic policy, restricting the
application of DRL in realistic scenario. To address this, we propose a
hierarchical RL framework where models at different levels vary in receptive
scope, action step length, and feedback period of reward. The upper layer model
accelerate CAVs to prevent them from being clashed, while the lower layer model
adjust the trends from upper layer model to avoid the change of mobile state
causing new conflicts. And the real action of CAV at each step is co-determined
by the trends from both levels, forming a real-time balance in the adversarial
process. The proposed model is proven effective in the experiment undertaken in
a complicated intersection with 4 branches and 4 lanes each branch, and show
better performance compared with baselines.
|
[
{
"version": "v1",
"created": "Thu, 5 May 2022 04:07:13 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Jun 2022 06:19:46 GMT"
},
{
"version": "v3",
"created": "Mon, 20 Jun 2022 07:31:04 GMT"
},
{
"version": "v4",
"created": "Sat, 23 Jul 2022 15:34:27 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Li",
"Guanzhou",
""
],
[
"Wu",
"Jianping",
""
],
[
"He",
"Yujing",
""
]
] |
new_dataset
| 0.999148 |
2205.03146
|
Piotr Mirowski
|
Piotr Mirowski, Dylan Banarse, Mateusz Malinowski, Simon Osindero,
Chrisantha Fernando
|
CLIP-CLOP: CLIP-Guided Collage and Photomontage
|
5 pages, 7 figures, published at the International Conference on
Computational Creativity (ICCC) 2022 as Short Paper: Demo
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The unabated mystique of large-scale neural networks, such as the CLIP dual
image-and-text encoder, popularized automatically generated art. Increasingly
more sophisticated generators enhanced the artworks' realism and visual
appearance, and creative prompt engineering enabled stylistic expression.
Guided by an artist-in-the-loop ideal, we design a gradient-based generator to
produce collages. It requires the human artist to curate libraries of image
patches and to describe (with prompts) the whole image composition, with the
option to manually adjust the patches' positions during generation, thereby
allowing humans to reclaim some control of the process and achieve greater
creative freedom. We explore the aesthetic potentials of high-resolution
collages, and provide an open-source Google Colab as an artistic tool.
|
[
{
"version": "v1",
"created": "Fri, 6 May 2022 11:33:49 GMT"
},
{
"version": "v2",
"created": "Thu, 19 May 2022 13:39:32 GMT"
},
{
"version": "v3",
"created": "Sun, 24 Jul 2022 14:47:50 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Mirowski",
"Piotr",
""
],
[
"Banarse",
"Dylan",
""
],
[
"Malinowski",
"Mateusz",
""
],
[
"Osindero",
"Simon",
""
],
[
"Fernando",
"Chrisantha",
""
]
] |
new_dataset
| 0.999233 |
2205.11925
|
Aldi Piroli
|
Aldi Piroli, Vinzenz Dallabetta, Marc Walessa, Daniel Meissner,
Johannes Kopp, Klaus Dietmayer
|
Robust 3D Object Detection in Cold Weather Conditions
|
Oral
|
2022 IEEE Intelligent Vehicles Symposium (IV)
|
10.1109/IV51971.2022.9827398
| null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Adverse weather conditions can negatively affect LiDAR-based object
detectors. In this work, we focus on the phenomenon of vehicle gas exhaust
condensation in cold weather conditions. This everyday effect can influence the
estimation of object sizes, orientations and introduce ghost object detections,
compromising the reliability of the state of the art object detectors. We
propose to solve this problem by using data augmentation and a novel training
loss term. To effectively train deep neural networks, a large set of labeled
data is needed. In case of adverse weather conditions, this process can be
extremely laborious and expensive. We address this issue in two steps: First,
we present a gas exhaust data generation method based on 3D surface
reconstruction and sampling which allows us to generate large sets of gas
exhaust clouds from a small pool of labeled data. Second, we introduce a point
cloud augmentation process that can be used to add gas exhaust to datasets
recorded in good weather conditions. Finally, we formulate a new training loss
term that leverages the augmented point cloud to increase object detection
robustness by penalizing predictions that include noise. In contrast to other
works, our method can be used with both grid-based and point-based detectors.
Moreover, since our approach does not require any network architecture changes,
inference times remain unchanged. Experimental results on real data show that
our proposed method greatly increases robustness to gas exhaust and noisy data.
|
[
{
"version": "v1",
"created": "Tue, 24 May 2022 09:37:07 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Jul 2022 14:18:03 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Piroli",
"Aldi",
""
],
[
"Dallabetta",
"Vinzenz",
""
],
[
"Walessa",
"Marc",
""
],
[
"Meissner",
"Daniel",
""
],
[
"Kopp",
"Johannes",
""
],
[
"Dietmayer",
"Klaus",
""
]
] |
new_dataset
| 0.993627 |
2205.14412
|
Yuepeng Qian
|
Yuepeng Qian, Shuaishuai Han, Gabriel Aguirre-Ollinger, Chenglong Fu
and Haoyong Yu
|
Design, Modelling, and Control of a Reconfigurable Rotary Series Elastic
Actuator with Nonlinear Stiffness for Assistive Robots
| null |
Mechatronics 86 (2022) 102872
|
10.1016/j.mechatronics.2022.102872
| null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In assistive robots, compliant actuator is a key component in establishing
safe and satisfactory physical human-robot interaction (pHRI). The performance
of compliant actuators largely depends on the stiffness of the elastic element.
Generally, low stiffness is desirable to achieve low impedance, high fidelity
of force control and safe pHRI, while high stiffness is required to ensure
sufficient force bandwidth and output force. These requirements, however, are
contradictory and often vary according to different tasks and conditions. In
order to address the contradiction of stiffness selection and improve
adaptability to different applications, we develop a reconfigurable rotary
series elastic actuator with nonlinear stiffness (RRSEAns) for assistive
robots. In this paper, an accurate model of the reconfigurable rotary series
elastic element (RSEE) is presented and the adjusting principles are
investigated, followed by detailed analysis and experimental validation. The
RRSEAns can provide a wide range of stiffness from 0.095 Nm/deg to 2.33 Nm/deg,
and different stiffness profiles can be yielded with respect to different
configuration of the reconfigurable RSEE. The overall performance of the
RRSEAns is verified by experiments on frequency response, torque control and
pHRI, which is adequate for most applications in assistive robots.
Specifically, the root-mean-square (RMS) error of the interaction torque
results as low as 0.07 Nm in transparent/human-in-charge mode, demonstrating
the advantages of the RRSEAns in pHRI.
|
[
{
"version": "v1",
"created": "Sat, 28 May 2022 12:11:23 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Qian",
"Yuepeng",
""
],
[
"Han",
"Shuaishuai",
""
],
[
"Aguirre-Ollinger",
"Gabriel",
""
],
[
"Fu",
"Chenglong",
""
],
[
"Yu",
"Haoyong",
""
]
] |
new_dataset
| 0.975422 |
2206.10735
|
G\"okberk Erdo\u{g}an
|
G\"okberk Erdo\u{g}an, Georg Maringer, Nikita Polyanskii
|
Signature Codes for a Noisy Adder Multiple Access Channel
|
12 pages, 0 figures, submitted to 2022 IEEE Information Theory
Workshop
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In this work, we consider $q$-ary signature codes of length $k$ and size $n$
for a noisy adder multiple access channel. A signature code in this model has
the property that any subset of codewords can be uniquely reconstructed based
on any vector that is obtained from the sum (over integers) of these codewords.
We show that there exists an algorithm to construct a signature code of length
$k = \frac{2n\log{3}}{(1-2\tau)\left(\log{n} + (q-1)\log{\frac{\pi}{2}}\right)}
+\mathcal{O}\left(\frac{n}{\log{n}(q+\log{n})}\right)$ capable of correcting
$\tau k$ errors at the channel output, where $0\le \tau < \frac{q-1}{2q}$.
Furthermore, we present an explicit construction of signature codewords with
polynomial complexity being able to correct up to $\left( \frac{q-1}{8q} -
\epsilon\right)k$ errors for a codeword length $k = \mathcal{O} \left (
\frac{n}{\log \log n} \right )$, where $\epsilon$ is a small non-negative
number. Moreover, we prove several non-existence results (converse bounds) for
$q$-ary signature codes enabling error correction.
|
[
{
"version": "v1",
"created": "Tue, 21 Jun 2022 21:17:49 GMT"
},
{
"version": "v2",
"created": "Sat, 23 Jul 2022 07:40:16 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Erdoğan",
"Gökberk",
""
],
[
"Maringer",
"Georg",
""
],
[
"Polyanskii",
"Nikita",
""
]
] |
new_dataset
| 0.986381 |
2207.00642
|
Leon Abdillah
|
Rahayu Agustina, Leon Andretti Abdillah
|
Analisis Kepuasan Pengguna Aplikasi Bintang Cash & Credit Menggunakan
Metode End User Computing Satisfaction (EUCS)
|
10 pages, conference (2021). arXiv admin note: substantial text
overlap with arXiv:2207.00006
| null | null | null |
cs.HC cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The use of android application technology has advanced rapidly in recent
years, making it one of the alternative media for distributing information in a
variety of industries, including e-commerce, that consumers may access at any
time and from any location. The Bintang Cash & Credit store in Palembang is one
of the stores that has already used the Android application. In EUCS there are
seven variables: content, accuracy, format, ease of use and timeliness,
security, and speed of response. The data of this research were collected by
distributing questionnaires to 95 respondents using a random sampling
technique. Furthermore, the data obtained were processed using SPSS version 25
software. The data analysis method used was a quantitative analysis method
using validity and reliability tests, classical assumption tests, multiple
regression tests, and hypothesis testing. From the results of this study, there
is a positive influence on the satisfaction of users of the Bintang Cash &
Credit application.
|
[
{
"version": "v1",
"created": "Mon, 27 Jun 2022 07:10:00 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Agustina",
"Rahayu",
""
],
[
"Abdillah",
"Leon Andretti",
""
]
] |
new_dataset
| 0.968985 |
2207.07861
|
Hongtao Wen
|
Hongtao Wen, Jianhang Yan, Wanli Peng, Yi Sun
|
TransGrasp: Grasp Pose Estimation of a Category of Objects by
Transferring Grasps from Only One Labeled Instance
|
Accepted to European Conference on Computer Vision (ECCV) 2022
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Grasp pose estimation is an important issue for robots to interact with the
real world. However, most of existing methods require exact 3D object models
available beforehand or a large amount of grasp annotations for training. To
avoid these problems, we propose TransGrasp, a category-level grasp pose
estimation method that predicts grasp poses of a category of objects by
labeling only one object instance. Specifically, we perform grasp pose transfer
across a category of objects based on their shape correspondences and propose a
grasp pose refinement module to further fine-tune grasp pose of grippers so as
to ensure successful grasps. Experiments demonstrate the effectiveness of our
method on achieving high-quality grasps with the transferred grasp poses. Our
code is available at https://github.com/yanjh97/TransGrasp.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 07:27:27 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Jul 2022 02:44:56 GMT"
},
{
"version": "v3",
"created": "Mon, 25 Jul 2022 07:46:20 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Wen",
"Hongtao",
""
],
[
"Yan",
"Jianhang",
""
],
[
"Peng",
"Wanli",
""
],
[
"Sun",
"Yi",
""
]
] |
new_dataset
| 0.972572 |
2207.08631
|
Chao Chen
|
Chao Chen, Yu-Shen Liu, Zhizhong Han
|
Latent Partition Implicit with Surface Codes for 3D Representation
|
20pages,14figures. Accepted by ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep implicit functions have shown remarkable shape modeling ability in
various 3D computer vision tasks. One drawback is that it is hard for them to
represent a 3D shape as multiple parts. Current solutions learn various
primitives and blend the primitives directly in the spatial space, which still
struggle to approximate the 3D shape accurately. To resolve this problem, we
introduce a novel implicit representation to represent a single 3D shape as a
set of parts in the latent space, towards both highly accurate and plausibly
interpretable shape modeling. Our insight here is that both the part learning
and the part blending can be conducted much easier in the latent space than in
the spatial space. We name our method Latent Partition Implicit (LPI), because
of its ability of casting the global shape modeling into multiple local part
modeling, which partitions the global shape unity. LPI represents a shape as
Signed Distance Functions (SDFs) using surface codes. Each surface code is a
latent code representing a part whose center is on the surface, which enables
us to flexibly employ intrinsic attributes of shapes or additional surface
properties. Eventually, LPI can reconstruct both the shape and the parts on the
shape, both of which are plausible meshes. LPI is a multi-level representation,
which can partition a shape into different numbers of parts after training. LPI
can be learned without ground truth signed distances, point normals or any
supervision for part partition. LPI outperforms the latest methods under the
widely used benchmarks in terms of reconstruction accuracy and modeling
interpretability. Our code, data and models are available at
https://github.com/chenchao15/LPI.
|
[
{
"version": "v1",
"created": "Mon, 18 Jul 2022 14:24:46 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 02:22:32 GMT"
},
{
"version": "v3",
"created": "Sat, 23 Jul 2022 08:22:27 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Chen",
"Chao",
""
],
[
"Liu",
"Yu-Shen",
""
],
[
"Han",
"Zhizhong",
""
]
] |
new_dataset
| 0.975097 |
2207.11280
|
Gerasimos Lampouras
|
Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta, Guchun Zhang,
Yinpeng Guo, Zhongqi Li, Qi Zhang, Meng Xiao, Bo Shen, Lin Li, Hao Yu, Li
Yan, Pingyi Zhou, Xin Wang, Yuchi Ma, Ignacio Iacobacci, Yasheng Wang,
Guangtai Liang, Jiansheng Wei, Xin Jiang, Qianxiang Wang, Qun Liu
|
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
|
27 pages
| null | null | null |
cs.LG cs.AI cs.CL cs.PL cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present PanGu-Coder, a pretrained decoder-only language model adopting the
PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of
programming language solutions given a natural language problem description. We
train PanGu-Coder using a two-stage strategy: the first stage employs Causal
Language Modelling (CLM) to pre-train on raw programming language data, while
the second stage uses a combination of Causal Language Modelling and Masked
Language Modelling (MLM) training objectives that focus on the downstream task
of text-to-code generation and train on loosely curated pairs of natural
language program definitions and code functions. Finally, we discuss
PanGu-Coder-FT, which is fine-tuned on a combination of competitive programming
problems and code with continuous integration tests. We evaluate PanGu-Coder
with a focus on whether it generates functionally correct programs and
demonstrate that it achieves equivalent or better performance than similarly
sized models, such as CodeX, while attending a smaller context window and
training on less data.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 18:08:16 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Christopoulou",
"Fenia",
""
],
[
"Lampouras",
"Gerasimos",
""
],
[
"Gritta",
"Milan",
""
],
[
"Zhang",
"Guchun",
""
],
[
"Guo",
"Yinpeng",
""
],
[
"Li",
"Zhongqi",
""
],
[
"Zhang",
"Qi",
""
],
[
"Xiao",
"Meng",
""
],
[
"Shen",
"Bo",
""
],
[
"Li",
"Lin",
""
],
[
"Yu",
"Hao",
""
],
[
"Yan",
"Li",
""
],
[
"Zhou",
"Pingyi",
""
],
[
"Wang",
"Xin",
""
],
[
"Ma",
"Yuchi",
""
],
[
"Iacobacci",
"Ignacio",
""
],
[
"Wang",
"Yasheng",
""
],
[
"Liang",
"Guangtai",
""
],
[
"Wei",
"Jiansheng",
""
],
[
"Jiang",
"Xin",
""
],
[
"Wang",
"Qianxiang",
""
],
[
"Liu",
"Qun",
""
]
] |
new_dataset
| 0.999734 |
2207.11300
|
Stefan Bosse
|
Stefan Bosse
|
JAM: The JavaScript Agent Machine for Distributed Computing and
Simulation with reactive and mobile Multi-agent Systems -- A Technical Report
| null | null | null | null |
cs.AI cs.DC cs.HC cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Agent-based modelling (ABM), simulation (ABS), and distributed computation
(ABC) are established methods. The Internet and Web-based technologies are
suitable carriers. This paper is a technical report with some tutorial aspects
of the JavaScript Agent Machine (JAM) platform and the programming of agents
with AgentJS, a sub-set of the widely used JavaScript programming language for
the programming of mobile state-based reactive agents. In addition to
explaining the motivation for particular design choices and introducing core
concepts of the architecture and the programming of agents in JavaScript, short
examples illustrate the power of the JAM platform and its components for the
deployment of large-scale multi-agent system in strong heterogeneous
environments like the Internet. JAM is suitable for the deployment in strong
heterogeneous and mobile environments. Finally, JAM can be used for ABC as well
as for ABS in an unified methodology, finally enabling mobile crowd sensing
coupled with simulation (ABS).
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 19:01:48 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Bosse",
"Stefan",
""
]
] |
new_dataset
| 0.996508 |
2207.11350
|
Li Zhou
|
Li Zhou, Gilles Barthe, Pierre-Yves Strub, Junyi Liu, Mingsheng Ying
|
CoqQ: Foundational Verification of Quantum Programs
| null | null | null | null |
cs.PL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
CoqQ is a framework for reasoning about quantum programs in the Coq proof
assistant. Its main components are: a deeply embedded quantum programming
language, in which classic quantum algorithms are easily expressed, and an
expressive program logic for proving properties of programs. CoqQ is
foundational: the program logic is formally proved sound with respect to a
denotational semantics based on state-of-art mathematical libraries (mathcomp
and mathcomp analysis). CoqQ is also practical: assertions can use Dirac
expressions, which eases concise specifications, and proofs can exploit local
and parallel reasoning, which minimizes verification effort. We illustrate the
applicability of CoqQ with many examples from the literature.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 21:41:11 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Zhou",
"Li",
""
],
[
"Barthe",
"Gilles",
""
],
[
"Strub",
"Pierre-Yves",
""
],
[
"Liu",
"Junyi",
""
],
[
"Ying",
"Mingsheng",
""
]
] |
new_dataset
| 0.99916 |
2207.11357
|
Molly Jane Nicholas
|
Molly Jane Nicholas, Eric Paulos
|
PREPRINT: Found Object Puppeteering as a Tool for Rapid Movement
Sketching in 3D Animation
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Both expert and novice animators have a need to engage in movement sketching
-- low-cost, rapid iteration on a character's movement style -- especially
early on in the ideation process. Yet animation tools currently focus on
low-level character control mechanisms rather than encouraging engagement with
and deep observation of movement. We identify Found Object puppeteering --
where puppeteers manipulate everyday physical objects with their hands -- as a
creative practice whose use of material "jigs" is uniquely well-positioned to
scaffold the novice animator's developing skills. In this paper, we draw on the
practice of an expert puppeteer practitioner to inform the design of a system
that incorporates physical objects into the animation workflow to scaffold
novices into diverse movement exploration while manipulating digital puppets.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 22:22:16 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Nicholas",
"Molly Jane",
""
],
[
"Paulos",
"Eric",
""
]
] |
new_dataset
| 0.998946 |
2207.11384
|
Xinyu Zhang
|
Xinyu Zhang, Jiangeng Huang, Yuanhao Huang, Kangyao Huang, Lei Yang,
Yan Han, Li Wang, Huaping Liu, Jianxi Luo and Jun Li
|
Intelligent Amphibious Ground-Aerial Vehicles: State of the Art
Technology for Future Transportation
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Amphibious ground-aerial vehicles fuse flying and driving modes to enable
more flexible air-land mobility and have received growing attention recently.
By analyzing the existing amphibious vehicles, we highlight the autonomous
fly-driving functionality for the effective uses of amphibious vehicles in
complex three-dimensional urban transportation systems. We review and summarize
the key enabling technologies for intelligent flying-driving in existing
amphibious vehicle designs, identify major technological barriers and propose
potential solutions for future research and innovation. This paper aims to
serve as a guide for research and development of intelligent amphibious
vehicles for urban transportation toward the future.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 00:57:34 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Zhang",
"Xinyu",
""
],
[
"Huang",
"Jiangeng",
""
],
[
"Huang",
"Yuanhao",
""
],
[
"Huang",
"Kangyao",
""
],
[
"Yang",
"Lei",
""
],
[
"Han",
"Yan",
""
],
[
"Wang",
"Li",
""
],
[
"Liu",
"Huaping",
""
],
[
"Luo",
"Jianxi",
""
],
[
"Li",
"Jun",
""
]
] |
new_dataset
| 0.9989 |
2207.11432
|
Christopher Mutschler
|
Sebastian Rietsch, Shih-Yuan Huang, Georgios Kontes, Axel Plinge,
Christopher Mutschler
|
Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for
Autonomous Driving
|
19 pages, 8 figures
| null | null | null |
cs.LG cs.AI cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reinforcement learning (RL) has shown to reach super human-level performance
across a wide range of tasks. However, unlike supervised machine learning,
learning strategies that generalize well to a wide range of situations remains
one of the most challenging problems for real-world RL. Autonomous driving (AD)
provides a multi-faceted experimental field, as it is necessary to learn the
correct behavior over many variations of road layouts and large distributions
of possible traffic situations, including individual driver personalities and
hard-to-predict traffic events. In this paper we propose a challenging
benchmark for generalizable RL for AD based on a configurable, flexible, and
performant code base. Our benchmark uses a catalog of randomized scenario
generators, including multiple mechanisms for road layout and traffic
variations, different numerical and visual observation types, distinct action
spaces, diverse vehicle models, and allows for use under static scenario
definitions. In addition to purely algorithmic insights, our
application-oriented benchmark also enables a better understanding of the
impact of design decisions such as action and observation space on the
generalizability of policies. Our benchmark aims to encourage researchers to
propose solutions that are able to successfully generalize across scenarios, a
task in which current RL methods fail. The code for the benchmark is available
at https://github.com/seawee1/driver-dojo.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 06:29:43 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Rietsch",
"Sebastian",
""
],
[
"Huang",
"Shih-Yuan",
""
],
[
"Kontes",
"Georgios",
""
],
[
"Plinge",
"Axel",
""
],
[
"Mutschler",
"Christopher",
""
]
] |
new_dataset
| 0.999607 |
2207.11455
|
Zhiheng Wu
|
Zhiheng Wu, Yue Lu, Xingyu Chen, Zhengxing Wu, Liwen Kang, and Junzhi
Yu
|
UC-OWOD: Unknown-Classified Open World Object Detection
|
Accepted to ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Open World Object Detection (OWOD) is a challenging computer vision problem
that requires detecting unknown objects and gradually learning the identified
unknown classes. However, it cannot distinguish unknown instances as multiple
unknown classes. In this work, we propose a novel OWOD problem called
Unknown-Classified Open World Object Detection (UC-OWOD). UC-OWOD aims to
detect unknown instances and classify them into different unknown classes.
Besides, we formulate the problem and devise a two-stage object detector to
solve UC-OWOD. First, unknown label-aware proposal and unknown-discriminative
classification head are used to detect known and unknown objects. Then,
similarity-based unknown classification and unknown clustering refinement
modules are constructed to distinguish multiple unknown classes. Moreover, two
novel evaluation protocols are designed to evaluate unknown-class detection.
Abundant experiments and visualizations prove the effectiveness of the proposed
method. Code is available at https://github.com/JohnWuzh/UC-OWOD.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 08:15:30 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Wu",
"Zhiheng",
""
],
[
"Lu",
"Yue",
""
],
[
"Chen",
"Xingyu",
""
],
[
"Wu",
"Zhengxing",
""
],
[
"Kang",
"Liwen",
""
],
[
"Yu",
"Junzhi",
""
]
] |
new_dataset
| 0.999343 |
2207.11466
|
Eran Kaufman Dr.
|
Eran Kaufman and Andrey Iaremenko
|
Anomaly Detection for Fraud in Cryptocurrency Time Series
| null | null | null | null |
cs.LG cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Since the inception of Bitcoin in 2009, the market of cryptocurrencies has
grown beyond initial expectations as daily trades exceed $10 billion. As
industries become automated, the need for an automated fraud detector becomes
very apparent. Detecting anomalies in real time prevents potential accidents
and economic losses. Anomaly detection in multivariate time series data poses a
particular challenge because it requires simultaneous consideration of temporal
dependencies and relationships between variables. Identifying an anomaly in
real time is not an easy task specifically because of the exact anomalistic
behavior they observe. Some points may present pointwise global or local
anomalistic behavior, while others may be anomalistic due to their frequency or
seasonal behavior or due to a change in the trend. In this paper we suggested
working on real time series of trades of Ethereum from specific accounts and
surveyed a large variety of different algorithms traditional and new. We
categorized them according to the strategy and the anomalistic behavior which
they search and showed that when bundling them together to different groups,
they can prove to be a good real-time detector with an alarm time of no longer
than a few seconds and with very high confidence.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 08:58:57 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Kaufman",
"Eran",
""
],
[
"Iaremenko",
"Andrey",
""
]
] |
new_dataset
| 0.990508 |
2207.11467
|
Zuoyue Li
|
Zuoyue Li, Tianxing Fan, Zhenqiang Li, Zhaopeng Cui, Yoichi Sato, Marc
Pollefeys, Martin R. Oswald
|
CompNVS: Novel View Synthesis with Scene Completion
|
ECCV 2022
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce a scalable framework for novel view synthesis from RGB-D images
with largely incomplete scene coverage. While generative neural approaches have
demonstrated spectacular results on 2D images, they have not yet achieved
similar photorealistic results in combination with scene completion where a
spatial 3D scene understanding is essential. To this end, we propose a
generative pipeline performing on a sparse grid-based neural scene
representation to complete unobserved scene parts via a learned distribution of
scenes in a 2.5D-3D-2.5D manner. We process encoded image features in 3D space
with a geometry completion network and a subsequent texture inpainting network
to extrapolate the missing area. Photorealistic image sequences can be finally
obtained via consistency-relevant differentiable rendering. Comprehensive
experiments show that the graphical outputs of our method outperform the state
of the art, especially within unobserved scene parts.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 09:03:13 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Li",
"Zuoyue",
""
],
[
"Fan",
"Tianxing",
""
],
[
"Li",
"Zhenqiang",
""
],
[
"Cui",
"Zhaopeng",
""
],
[
"Sato",
"Yoichi",
""
],
[
"Pollefeys",
"Marc",
""
],
[
"Oswald",
"Martin R.",
""
]
] |
new_dataset
| 0.987249 |
2207.11521
|
Gabriel Lindel\"of
|
Gabriel Lindel\"of, Talayeh Aledavood, Barbara Keller
|
Vaccine Discourse on Twitter During the COVID-19 Pandemic
|
17 pages, 7 figures
| null |
10.2196/41319
| null |
cs.CY cs.CL cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Since the onset of the COVID-19 pandemic, vaccines have been an important
topic in public discourse. The discussions around vaccines are polarized as
some see them as an important measure to end the pandemic, and others are
hesitant or find them harmful. This study investigates posts related to
COVID-19 vaccines on Twitter and focuses on those which have a negative stance
toward vaccines. A dataset of 16,713,238 English tweets related to COVID-19
vaccines was collected covering the period from March 1, 2020, to July 31,
2021. We used the Scikit-learn Python library to apply a support vector machine
(SVM) classifier to identify the tweets with a negative stance toward the
COVID-19 vaccines. A total of 5,163 tweets were used to train the classifier,
out of which a subset of 2,484 tweets were manually annotated by us and made
publicly available. We used the BERTtopic model to extract and investigate the
topics discussed within the negative tweets and how they changed over time. We
show that the negativity with respect to COVID-19 vaccines has decreased over
time along with the vaccine roll-outs. We identify 37 topics of discussion and
present their respective importance over time. We show that popular topics
consist of conspiratorial discussions such as 5G towers and microchips, but
also contain legitimate concerns around vaccination safety and side effects as
well as concerns about policies. Our study shows that even unpopular opinions
or conspiracy theories can become widespread when paired with a widely popular
discussion topic such as COVID-19 vaccines. Understanding the concerns and the
discussed topics and how they change over time is essential for policymakers
and public health authorities to provide better and in-time information and
policies, to facilitate vaccination of the population in future similar crises.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 13:50:51 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Lindelöf",
"Gabriel",
""
],
[
"Aledavood",
"Talayeh",
""
],
[
"Keller",
"Barbara",
""
]
] |
new_dataset
| 0.999809 |
2207.11528
|
Miguel Arana-Catania
|
M. Arana-Catania, F.A. Van Lier, Rob Procter
|
Supporting peace negotiations in the Yemen war through machine learning
|
28 pages, 16 figures, 2 tables. An earlier version of this paper was
presented at the Data for Policy Conference, September, 2021. Current version
to appear in Data & Policy journal
| null | null | null |
cs.CL cs.CY cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Today's conflicts are becoming increasingly complex, fluid and fragmented,
often involving a host of national and international actors with multiple and
often divergent interests. This development poses significant challenges for
conflict mediation, as mediators struggle to make sense of conflict dynamics,
such as the range of conflict parties and the evolution of their political
positions, the distinction between relevant and less relevant actors in
peace-making, or the identification of key conflict issues and their
interdependence. International peace efforts appear ill-equipped to
successfully address these challenges. While technology is already being
experimented with and used in a range of conflict related fields, such as
conflict predicting or information gathering, less attention has been given to
how technology can contribute to conflict mediation. This case study
contributes to emerging research on the use of state-of-the-art machine
learning technologies and techniques in conflict mediation processes. Using
dialogue transcripts from peace negotiations in Yemen, this study shows how
machine-learning can effectively support mediating teams by providing them with
tools for knowledge management, extraction and conflict analysis. Apart from
illustrating the potential of machine learning tools in conflict mediation, the
paper also emphasises the importance of interdisciplinary and participatory,
co-creation methodology for the development of context-sensitive and targeted
tools and to ensure meaningful and responsible implementation.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 14:24:38 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Arana-Catania",
"M.",
""
],
[
"Van Lier",
"F. A.",
""
],
[
"Procter",
"Rob",
""
]
] |
new_dataset
| 0.999445 |
2207.11537
|
Aryslan Malik
|
Jared Herron, Daniel Lopez, Jarred Jordan, Jillian Rudy, Aryslan
Malik, Daniel Posada, Mehran Andalibi, Troy Henderson
|
RGB-D Robotic Pose Estimation For a Servicing Robotic Arm
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A large number of robotic and human-assisted missions to the Moon and Mars
are forecast. NASA's efforts to learn about the geology and makeup of these
celestial bodies rely heavily on the use of robotic arms. The safety and
redundancy aspects will be crucial when humans will be working alongside the
robotic explorers. Additionally, robotic arms are crucial to satellite
servicing and planned orbit debris mitigation missions. The goal of this work
is to create a custom Computer Vision (CV) based Artificial Neural Network
(ANN) that would be able to rapidly identify the posture of a 7 Degree of
Freedom (DoF) robotic arm from a single (RGB-D) image - just like humans can
easily identify if an arm is pointing in some general direction. The Sawyer
robotic arm is used for developing and training this intelligent algorithm.
Since Sawyer's joint space spans 7 dimensions, it is an insurmountable task to
cover the entire joint configuration space. In this work, orthogonal arrays are
used, similar to the Taguchi method, to efficiently span the joint space with
the minimal number of training images. This ``optimally'' generated database is
used to train the custom ANN and its degree of accuracy is on average equal to
twice the smallest joint displacement step used for database generation. A
pre-trained ANN will be useful for estimating the postures of robotic
manipulators used on space stations, spacecraft, and rovers as an auxiliary
tool or for contingency plans.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 15:03:16 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Herron",
"Jared",
""
],
[
"Lopez",
"Daniel",
""
],
[
"Jordan",
"Jarred",
""
],
[
"Rudy",
"Jillian",
""
],
[
"Malik",
"Aryslan",
""
],
[
"Posada",
"Daniel",
""
],
[
"Andalibi",
"Mehran",
""
],
[
"Henderson",
"Troy",
""
]
] |
new_dataset
| 0.996653 |
2207.11541
|
Jingwei Wang
|
Tianle Ni, Jingwei Wang, Yunlong Ma, Shuang Wang, Min Liu, and Weiming
Shen
|
FastATDC: Fast Anomalous Trajectory Detection and Classification
|
6 pages, 4 figures, accepted by 2022 IEEE 18th International
Conference on Automation Science and Engineering (CASE)
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automated detection of anomalous trajectories is an important problem with
considerable applications in intelligent transportation systems. Many existing
studies have focused on distinguishing anomalous trajectories from normal
trajectories, ignoring the large differences between anomalous trajectories. A
recent study has made great progress in identifying abnormal trajectory
patterns and proposed a two-stage algorithm for anomalous trajectory detection
and classification (ATDC). This algorithm has excellent performance but suffers
from a few limitations, such as high time complexity and poor interpretation.
Here, we present a careful theoretical and empirical analysis of the ATDC
algorithm, showing that the calculation of anomaly scores in both stages can be
simplified, and that the second stage of the algorithm is much more important
than the first stage. Hence, we develop a FastATDC algorithm that introduces a
random sampling strategy in both stages. Experimental results show that
FastATDC is 10 to 20 times faster than ATDC on real datasets. Moreover,
FastATDC outperforms the baseline algorithms and is comparable to the ATDC
algorithm.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 15:32:33 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Ni",
"Tianle",
""
],
[
"Wang",
"Jingwei",
""
],
[
"Ma",
"Yunlong",
""
],
[
"Wang",
"Shuang",
""
],
[
"Liu",
"Min",
""
],
[
"Shen",
"Weiming",
""
]
] |
new_dataset
| 0.987626 |
2207.11545
|
Hanzhao Wang
|
Hanzhao Wang, Xiaocheng Li, Kalyan Talluri
|
Learning to Sell a Focal-ancillary Combination
| null | null | null | null |
cs.LG cs.IR econ.GN q-fin.EC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A number of products are sold in the following sequence: First a focal
product is shown, and if the customer purchases, one or more ancillary products
are displayed for purchase. A prominent example is the sale of an airline
ticket, where first the flight is shown, and when chosen, a number of
ancillaries such as cabin or hold bag options, seat selection, insurance etc.
are presented. The firm has to decide on a sale format -- whether to sell them
in sequence unbundled, or together as a bundle -- and how to price the focal
and ancillary products, separately or as a bundle. Since the ancillary is
considered by the customer only after the purchase of the focal product, the
sale strategy chosen by the firm creates an information and learning dependency
between the products: for instance, offering only a bundle would preclude
learning customers' valuation for the focal and ancillary products
individually. In this paper we study learning strategies for such focal and
ancillary item combinations under the following scenarios: (a) pure unbundling
to all customers, (b) personalized mechanism, where, depending on some observed
features of the customers, the two products are presented and priced as a
bundle or in sequence, (c) initially unbundling (for all customers), and switch
to bundling (if more profitable) permanently once during the horizon. We design
pricing and decisions algorithms for all three scenarios, with regret upper
bounded by $O(d \sqrt{T} \log T)$, and an optimal switching time for the third
scenario.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 15:55:16 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Wang",
"Hanzhao",
""
],
[
"Li",
"Xiaocheng",
""
],
[
"Talluri",
"Kalyan",
""
]
] |
new_dataset
| 0.990597 |
2207.11565
|
Marcin Pietron
|
Michal Karwatowski and Marcin Pietron
|
Context based lemmatizer for Polish language
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Lemmatization is the process of grouping together the inflected forms of a
word so they can be analysed as a single item, identified by the word's lemma,
or dictionary form. In computational linguistics, lemmatisation is the
algorithmic process of determining the lemma of a word based on its intended
meaning. Unlike stemming, lemmatisation depends on correctly identifying the
intended part of speech and meaning of a word in a sentence, as well as within
the larger context surrounding that sentence. As a result, developing efficient
lemmatisation algorithm is the complex task. In recent years it can be observed
that deep learning models used for this task outperform other methods including
machine learning algorithms. In this paper the polish lemmatizer based on
Google T5 model is presented. The training was run with different context
lengths. The model achieves the best results for polish language lemmatisation
process.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 18:02:16 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Karwatowski",
"Michal",
""
],
[
"Pietron",
"Marcin",
""
]
] |
new_dataset
| 0.964026 |
2207.11577
|
Mostafa Shabani
|
Mostafa Shabani, Dat Thanh Tran, Juho Kanniainen, Alexandros Iosifidis
|
Augmented Bilinear Network for Incremental Multi-Stock Time-Series
Classification
| null | null | null | null |
cs.LG q-fin.ST
|
http://creativecommons.org/licenses/by/4.0/
|
Deep Learning models have become dominant in tackling financial time-series
analysis problems, overturning conventional machine learning and statistical
methods. Most often, a model trained for one market or security cannot be
directly applied to another market or security due to differences inherent in
the market conditions. In addition, as the market evolves through time, it is
necessary to update the existing models or train new ones when new data is made
available. This scenario, which is inherent in most financial forecasting
applications, naturally raises the following research question: How to
efficiently adapt a pre-trained model to a new set of data while retaining
performance on the old data, especially when the old data is not accessible? In
this paper, we propose a method to efficiently retain the knowledge available
in a neural network pre-trained on a set of securities and adapt it to achieve
high performance in new ones. In our method, the prior knowledge encoded in a
pre-trained neural network is maintained by keeping existing connections fixed,
and this knowledge is adjusted for the new securities by a set of augmented
connections, which are optimized using the new data. The auxiliary connections
are constrained to be of low rank. This not only allows us to rapidly optimize
for the new task but also reduces the storage and run-time complexity during
the deployment phase. The efficiency of our approach is empirically validated
in the stock mid-price movement prediction problem using a large-scale limit
order book dataset. Experimental results show that our approach enhances
prediction performance as well as reduces the overall number of network
parameters.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 18:54:10 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Shabani",
"Mostafa",
""
],
[
"Tran",
"Dat Thanh",
""
],
[
"Kanniainen",
"Juho",
""
],
[
"Iosifidis",
"Alexandros",
""
]
] |
new_dataset
| 0.955063 |
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