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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2303.01091
|
Gaochao Song
|
Gaochao Song, Luo Zhang, Ran Su, Jianfeng Shi, Ying He, Qian Sun
|
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free
Upsampling Module in Arbitrary-scale Image Super-Resolution
|
Accepted by CVPR 2023. 11 pages
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Implicit neural representation (INR) is a popular approach for
arbitrary-scale image super-resolution (SR), as a key component of INR,
position encoding improves its representation ability. Motivated by position
encoding, we propose orthogonal position encoding (OPE) - an extension of
position encoding - and an OPE-Upscale module to replace the INR-based
upsampling module for arbitrary-scale image super-resolution. Same as INR, our
OPE-Upscale Module takes 2D coordinates and latent code as inputs; however it
does not require training parameters. This parameter-free feature allows the
OPE-Upscale Module to directly perform linear combination operations to
reconstruct an image in a continuous manner, achieving an arbitrary-scale image
reconstruction. As a concise SR framework, our method has high computing
efficiency and consumes less memory comparing to the state-of-the-art (SOTA),
which has been confirmed by extensive experiments and evaluations. In addition,
our method has comparable results with SOTA in arbitrary scale image
super-resolution. Last but not the least, we show that OPE corresponds to a set
of orthogonal basis, justifying our design principle.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 09:26:14 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Song",
"Gaochao",
""
],
[
"Zhang",
"Luo",
""
],
[
"Su",
"Ran",
""
],
[
"Shi",
"Jianfeng",
""
],
[
"He",
"Ying",
""
],
[
"Sun",
"Qian",
""
]
] |
new_dataset
| 0.958137 |
2303.01162
|
V\'it Kr\'atk\'y
|
V\'it Kr\'atk\'y, Pavel Petr\'a\v{c}ek, Vojt\v{e}ch Spurn\'y, Martin
Saska
|
Autonomous Reflectance Transformation Imaging by a Team of Unmanned
Aerial Vehicles
| null |
IEEE Robotics and Automation Letters, vol. 5, no. 2, pp.
2302-2309, 2020
|
10.1109/LRA.2020.2970646
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A Reflectance Transformation Imaging technique (RTI) realized by multi-rotor
Unmanned Aerial Vehicles (UAVs) with a focus on deployment in difficult to
access buildings is presented in this letter. RTI is a computational
photographic method that captures a surface shape and color of a subject and
enables its interactive re-lighting from any direction in a software viewer,
revealing details that are not visible with the naked eye. The input of RTI is
a set of images captured by a static camera, each one under illumination from a
different known direction. We present an innovative approach applying two
multi-rotor UAVs to perform this scanning procedure in locations that are
hardly accessible or even inaccessible for people. The proposed system is
designed for its safe deployment within real-world scenarios in historical
buildings with priceless historical value.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 11:09:14 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Krátký",
"Vít",
""
],
[
"Petráček",
"Pavel",
""
],
[
"Spurný",
"Vojtěch",
""
],
[
"Saska",
"Martin",
""
]
] |
new_dataset
| 0.998598 |
2303.01166
|
Zhixing Hou
|
Zhixing Hou, Yuzhang Shang, Tian Gao, Yan Yan
|
BPT: Binary Point Cloud Transformer for Place Recognition
|
Submitted to the IEEE/RSJ International Conference on Intelligent
Robots (IROS 2023)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Place recognition, an algorithm to recognize the re-visited places, plays the
role of back-end optimization trigger in a full SLAM system. Many works
equipped with deep learning tools, such as MLP, CNN, and transformer, have
achieved great improvements in this research field. Point cloud transformer is
one of the excellent frameworks for place recognition applied in robotics, but
with large memory consumption and expensive computation, it is adverse to
widely deploy the various point cloud transformer networks in mobile or
embedded devices. To solve this issue, we propose a binary point cloud
transformer for place recognition. As a result, a 32-bit full-precision model
can be reduced to a 1-bit model with less memory occupation and faster
binarized bitwise operations. To our best knowledge, this is the first binary
point cloud transformer that can be deployed on mobile devices for online
applications such as place recognition. Experiments on several standard
benchmarks demonstrate that the proposed method can get comparable results with
the corresponding full-precision transformer model and even outperform some
full-precision deep learning methods. For example, the proposed method achieves
93.28% at the top @1% and 85.74% at the top @1% on the Oxford RobotCar dataset
in terms of the metric of the average recall rate. Meanwhile, the size and
floating point operations of the model with the same transformer structure
reduce 56.1% and 34.1% respectively from original precision to binary
precision.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 11:15:59 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Hou",
"Zhixing",
""
],
[
"Shang",
"Yuzhang",
""
],
[
"Gao",
"Tian",
""
],
[
"Yan",
"Yan",
""
]
] |
new_dataset
| 0.997985 |
2303.01173
|
Jack Saunders Mr
|
Jack Saunders, Lo\"ic Prenevost, \"Ozg\"ur \c{S}im\c{s}ek, Alan
Hunter, and Wenbin Li
|
Resource-Constrained Station-Keeping for Helium Balloons using
Reinforcement Learning
| null | null | null | null |
cs.RO cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
High altitude balloons have proved useful for ecological aerial surveys,
atmospheric monitoring, and communication relays. However, due to weight and
power constraints, there is a need to investigate alternate modes of propulsion
to navigate in the stratosphere. Very recently, reinforcement learning has been
proposed as a control scheme to maintain the balloon in the region of a fixed
location, facilitated through diverse opposing wind-fields at different
altitudes. Although air-pump based station keeping has been explored, there is
no research on the control problem for venting and ballasting actuated
balloons, which is commonly used as a low-cost alternative. We show how
reinforcement learning can be used for this type of balloon. Specifically, we
use the soft actor-critic algorithm, which on average is able to station-keep
within 50\;km for 25\% of the flight, consistent with state-of-the-art.
Furthermore, we show that the proposed controller effectively minimises the
consumption of resources, thereby supporting long duration flights. We frame
the controller as a continuous control reinforcement learning problem, which
allows for a more diverse range of trajectories, as opposed to current
state-of-the-art work, which uses discrete action spaces. Furthermore, through
continuous control, we can make use of larger ascent rates which are not
possible using air-pumps. The desired ascent-rate is decoupled into desired
altitude and time-factor to provide a more transparent policy, compared to
low-level control commands used in previous works. Finally, by applying the
equations of motion, we establish appropriate thresholds for venting and
ballasting to prevent the agent from exploiting the environment. More
specifically, we ensure actions are physically feasible by enforcing
constraints on venting and ballasting.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 11:35:59 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Saunders",
"Jack",
""
],
[
"Prenevost",
"Loïc",
""
],
[
"Şimşek",
"Özgür",
""
],
[
"Hunter",
"Alan",
""
],
[
"Li",
"Wenbin",
""
]
] |
new_dataset
| 0.993163 |
2303.01177
|
V\'it Kr\'atk\'y
|
V\'it Kr\'atk\'y, Alfonso Alc\'antara, Jes\'us Capit\'an, Petr
\v{S}t\v{e}p\'an, Martin Saska, An\'ibal Ollero
|
Autonomous Aerial Filming With Distributed Lighting by a Team of
Unmanned Aerial Vehicles
| null |
IEEE Robotics and Automation Letters, vol. 6, no. 4, pp.
7580-7587, 2021
|
10.1109/LRA.2021.3098811
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This letter describes a method for autonomous aerial cinematography with
distributed lighting by a team of unmanned aerial vehicles (UAVs). Although
camera-carrying multi-rotor helicopters have become commonplace in
cinematography, their usage is limited to scenarios with sufficient natural
light or of lighting provided by static artificial lights. We propose to use a
formation of unmanned aerial vehicles as a tool for filming a target under
illumination from various directions, which is one of the fundamental
techniques of traditional cinematography. We decompose the multi-UAV trajectory
optimization problem to tackle non-linear cinematographic aspects and obstacle
avoidance at separate stages, which allows us to re-plan in real time and react
to changes in dynamic environments. The performance of our method has been
evaluated in realistic simulation scenarios and field experiments, where we
show how it increases the quality of the shots and that it is capable of
planning safe trajectories even in cluttered environments.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 11:47:33 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Krátký",
"Vít",
""
],
[
"Alcántara",
"Alfonso",
""
],
[
"Capitán",
"Jesús",
""
],
[
"Štěpán",
"Petr",
""
],
[
"Saska",
"Martin",
""
],
[
"Ollero",
"Aníbal",
""
]
] |
new_dataset
| 0.997127 |
2303.01241
|
Runcong Zhao
|
Runcong Zhao, Miguel Arana-Catania, Lixing Zhu, Elena Kochkina, Lin
Gui, Arkaitz Zubiaga, Rob Procter, Maria Liakata and Yulan He
|
PANACEA: An Automated Misinformation Detection System on COVID-19
| null | null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In this demo, we introduce a web-based misinformation detection system
PANACEA on COVID-19 related claims, which has two modules, fact-checking and
rumour detection. Our fact-checking module, which is supported by novel natural
language inference methods with a self-attention network, outperforms
state-of-the-art approaches. It is also able to give automated veracity
assessment and ranked supporting evidence with the stance towards the claim to
be checked. In addition, PANACEA adapts the bi-directional graph convolutional
networks model, which is able to detect rumours based on comment networks of
related tweets, instead of relying on the knowledge base. This rumour detection
module assists by warning the users in the early stages when a knowledge base
may not be available.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 21:53:48 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Zhao",
"Runcong",
""
],
[
"Arana-Catania",
"Miguel",
""
],
[
"Zhu",
"Lixing",
""
],
[
"Kochkina",
"Elena",
""
],
[
"Gui",
"Lin",
""
],
[
"Zubiaga",
"Arkaitz",
""
],
[
"Procter",
"Rob",
""
],
[
"Liakata",
"Maria",
""
],
[
"He",
"Yulan",
""
]
] |
new_dataset
| 0.991103 |
2303.01243
|
Nicolas Kourtellis Ph.D.
|
Souvik Paul and Nicolas Kourtellis
|
Poster: Sponge ML Model Attacks of Mobile Apps
|
2 pages, 6 figures. Proceedings of the 24th International Workshop on
Mobile Computing Systems and Applications (HotMobile). Feb. 2023
| null |
10.1145/3572864.3581586
| null |
cs.LG cs.CR cs.PF
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Machine Learning (ML)-powered apps are used in pervasive devices such as
phones, tablets, smartwatches and IoT devices. Recent advances in
collaborative, distributed ML such as Federated Learning (FL) attempt to solve
privacy concerns of users and data owners, and thus used by tech industry
leaders such as Google, Facebook and Apple. However, FL systems and models are
still vulnerable to adversarial membership and attribute inferences and model
poisoning attacks, especially in FL-as-a-Service ecosystems recently proposed,
which can enable attackers to access multiple ML-powered apps. In this work, we
focus on the recently proposed Sponge attack: It is designed to soak up energy
consumed while executing inference (not training) of ML model, without
hampering the classifier's performance. Recent work has shown sponge attacks on
ASCI-enabled GPUs can potentially escalate the power consumption and inference
time. For the first time, in this work, we investigate this attack in the
mobile setting and measure the effect it can have on ML models running inside
apps on mobile devices.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 15:12:56 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Paul",
"Souvik",
""
],
[
"Kourtellis",
"Nicolas",
""
]
] |
new_dataset
| 0.993398 |
2303.01330
|
Jingping Wang
|
Tingrui Zhang, Jingping Wang, Chao Xu, Alan Gao, Fei Gao
|
Continuous Implicit SDF Based Any-shape Robot Trajectory Optimization
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Optimization-based trajectory generation methods are widely used in
whole-body planning for robots. However, existing work either oversimplifies
the robot's geometry and environment representation, resulting in a
conservative trajectory, or suffers from a huge overhead in maintaining
additional information such as the Signed Distance Field (SDF). To bridge the
gap, we consider the robot as an implicit function, with its surface boundary
represented by the zero-level set of its SDF. Based on this, we further employ
another implicit function to lazily compute the signed distance to the swept
volume generated by the robot and its trajectory. The computation is efficient
by exploiting continuity in space-time, and the implicit function guarantees
precise and continuous collision evaluation even for nonconvex robots with
complex surfaces. Furthermore, we propose a trajectory optimization pipeline
applicable to the implicit SDF. Simulation and real-world experiments validate
the high performance of our approach for arbitrarily shaped robot trajectory
optimization.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 15:08:00 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Zhang",
"Tingrui",
""
],
[
"Wang",
"Jingping",
""
],
[
"Xu",
"Chao",
""
],
[
"Gao",
"Alan",
""
],
[
"Gao",
"Fei",
""
]
] |
new_dataset
| 0.985251 |
2303.01331
|
Benjamin Joffe
|
Benjamin Joffe and Konrad Ahlin
|
Canonical mapping as a general-purpose object descriptor for robotic
manipulation
| null | null | null | null |
cs.RO cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Perception is an essential part of robotic manipulation in a semi-structured
environment. Traditional approaches produce a narrow task-specific prediction
(e.g., object's 6D pose), that cannot be adapted to other tasks and is
ill-suited for deformable objects. In this paper, we propose using canonical
mapping as a near-universal and flexible object descriptor. We demonstrate that
common object representations can be derived from a single pre-trained
canonical mapping model, which in turn can be generated with minimal manual
effort using an automated data generation and training pipeline. We perform a
multi-stage experiment using two robot arms that demonstrate the robustness of
the perception approach and the ways it can inform the manipulation strategy,
thus serving as a powerful foundation for general-purpose robotic manipulation.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 15:09:25 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Joffe",
"Benjamin",
""
],
[
"Ahlin",
"Konrad",
""
]
] |
new_dataset
| 0.952518 |
2303.01377
|
Daniel Sens
|
Daniel Sens and Ario Sadafi, Francesco Paolo Casale, Nassir Navab,
Carsten Marr
|
BEL: A Bag Embedding Loss for Transformer enhances Multiple Instance
Whole Slide Image Classification
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multiple Instance Learning (MIL) has become the predominant approach for
classification tasks on gigapixel histopathology whole slide images (WSIs).
Within the MIL framework, single WSIs (bags) are decomposed into patches
(instances), with only WSI-level annotation available. Recent MIL approaches
produce highly informative bag level representations by utilizing the
transformer architecture's ability to model the dependencies between instances.
However, when applied to high magnification datasets, problems emerge due to
the large number of instances and the weak supervisory learning signal. To
address this problem, we propose to additionally train transformers with a
novel Bag Embedding Loss (BEL). BEL forces the model to learn a discriminative
bag-level representation by minimizing the distance between bag embeddings of
the same class and maximizing the distance between different classes. We
evaluate BEL with the Transformer architecture TransMIL on two publicly
available histopathology datasets, BRACS and CAMELYON17. We show that with BEL,
TransMIL outperforms the baseline models on both datasets, thus contributing to
the clinically highly relevant AI-based tumor classification of histological
patient material.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 16:02:55 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Sens",
"Daniel",
""
],
[
"Sadafi",
"Ario",
""
],
[
"Casale",
"Francesco Paolo",
""
],
[
"Navab",
"Nassir",
""
],
[
"Marr",
"Carsten",
""
]
] |
new_dataset
| 0.995608 |
2303.01396
|
Zongtao He
|
Zongtao He, Liuyi Wang, Shu Li, Qingqing Yan, Chengju Liu and Qijun
Chen
|
MLANet: Multi-Level Attention Network with Sub-instruction for
Continuous Vision-and-Language Navigation
| null | null | null | null |
cs.CV cs.CL cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-and-Language Navigation (VLN) aims to develop intelligent agents to
navigate in unseen environments only through language and vision supervision.
In the recently proposed continuous settings (continuous VLN), the agent must
act in a free 3D space and faces tougher challenges like real-time execution,
complex instruction understanding, and long action sequence prediction. For a
better performance in continuous VLN, we design a multi-level instruction
understanding procedure and propose a novel model, Multi-Level Attention
Network (MLANet). The first step of MLANet is to generate sub-instructions
efficiently. We design a Fast Sub-instruction Algorithm (FSA) to segment the
raw instruction into sub-instructions and generate a new sub-instruction
dataset named ``FSASub". FSA is annotation-free and faster than the current
method by 70 times, thus fitting the real-time requirement in continuous VLN.
To solve the complex instruction understanding problem, MLANet needs a global
perception of the instruction and observations. We propose a Multi-Level
Attention (MLA) module to fuse vision, low-level semantics, and high-level
semantics, which produce features containing a dynamic and global comprehension
of the task. MLA also mitigates the adverse effects of noise words, thus
ensuring a robust understanding of the instruction. To correctly predict
actions in long trajectories, MLANet needs to focus on what sub-instruction is
being executed every step. We propose a Peak Attention Loss (PAL) to improve
the flexible and adaptive selection of the current sub-instruction. PAL
benefits the navigation agent by concentrating its attention on the local
information, thus helping the agent predict the most appropriate actions. We
train and test MLANet in the standard benchmark. Experiment results show MLANet
outperforms baselines by a significant margin.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 16:26:14 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"He",
"Zongtao",
""
],
[
"Wang",
"Liuyi",
""
],
[
"Li",
"Shu",
""
],
[
"Yan",
"Qingqing",
""
],
[
"Liu",
"Chengju",
""
],
[
"Chen",
"Qijun",
""
]
] |
new_dataset
| 0.997264 |
2303.01428
|
Christoforos Mavrogiannis
|
Sidharth Talia, Arnav Thareja, Christoforos Mavrogiannis, Matt
Schmittle, Siddhartha S. Srinivasa
|
PuSHR: A Multirobot System for Nonprehensile Rearrangement
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We focus on the problem of rearranging a set of objects with a team of
car-like robot pushers built using off-the-shelf components. Maintaining
control of pushed objects while avoiding collisions in a tight space demands
highly coordinated motion that is challenging to execute on constrained
hardware. Centralized replanning approaches become intractable even for
small-sized problems whereas decentralized approaches often get stuck in
deadlocks. Our key insight is that by carefully assigning pushing tasks to
robots, we could reduce the complexity of the rearrangement task, enabling
robust performance via scalable decentralized control. Based on this insight,
we built PuSHR, a system that optimally assigns pushing tasks and trajectories
to robots offline, and performs trajectory tracking via decentralized control
online. Through an ablation study in simulation, we demonstrate that PuSHR
dominates baselines ranging from purely decentralized to fully decentralized in
terms of success rate and time efficiency across challenging tasks with up to 4
robots. Hardware experiments demonstrate the transfer of our system to the real
world and highlight its robustness to model inaccuracies. Our code can be found
at https://github.com/prl-mushr/pushr, and videos from our experiments at
https://youtu.be/DIWmZerF_O8.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 17:31:42 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Talia",
"Sidharth",
""
],
[
"Thareja",
"Arnav",
""
],
[
"Mavrogiannis",
"Christoforos",
""
],
[
"Schmittle",
"Matt",
""
],
[
"Srinivasa",
"Siddhartha S.",
""
]
] |
new_dataset
| 0.950084 |
2303.01432
|
Ryo Kamoi
|
Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, Greg Durrett
|
WiCE: Real-World Entailment for Claims in Wikipedia
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Models for textual entailment have increasingly been applied to settings like
fact-checking, presupposition verification in question answering, and
validating that generation models' outputs are faithful to a source. However,
such applications are quite far from the settings that existing datasets are
constructed in. We propose WiCE, a new textual entailment dataset centered
around verifying claims in text, built on real-world claims and evidence in
Wikipedia with fine-grained annotations. We collect sentences in Wikipedia that
cite one or more webpages and annotate whether the content on those pages
entails those sentences. Negative examples arise naturally, from slight
misinterpretation of text to minor aspects of the sentence that are not
attested in the evidence. Our annotations are over sub-sentence units of the
hypothesis, decomposed automatically by GPT-3, each of which is labeled with a
subset of evidence sentences from the source document. We show that real claims
in our dataset involve challenging verification problems, and we benchmark
existing approaches on this dataset. In addition, we show that reducing the
complexity of claims by decomposing them by GPT-3 can improve entailment
models' performance on various domains.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 17:45:32 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Kamoi",
"Ryo",
""
],
[
"Goyal",
"Tanya",
""
],
[
"Rodriguez",
"Juan Diego",
""
],
[
"Durrett",
"Greg",
""
]
] |
new_dataset
| 0.993539 |
2303.01480
|
Jiaming Zhang
|
Jiaming Zhang, Ruiping Liu, Hao Shi, Kailun Yang, Simon Rei{\ss},
Kunyu Peng, Haodong Fu, Kaiwei Wang, Rainer Stiefelhagen
|
Delivering Arbitrary-Modal Semantic Segmentation
|
Accepted by CVPR 2023. Dataset and our code are at:
https://jamycheung.github.io/DELIVER.html
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multimodal fusion can make semantic segmentation more robust. However, fusing
an arbitrary number of modalities remains underexplored. To delve into this
problem, we create the DeLiVER arbitrary-modal segmentation benchmark, covering
Depth, LiDAR, multiple Views, Events, and RGB. Aside from this, we provide this
dataset in four severe weather conditions as well as five sensor failure cases
to exploit modal complementarity and resolve partial outages. To make this
possible, we present the arbitrary cross-modal segmentation model CMNeXt. It
encompasses a Self-Query Hub (SQ-Hub) designed to extract effective information
from any modality for subsequent fusion with the RGB representation and adds
only negligible amounts of parameters (~0.01M) per additional modality. On top,
to efficiently and flexibly harvest discriminative cues from the auxiliary
modalities, we introduce the simple Parallel Pooling Mixer (PPX). With
extensive experiments on a total of six benchmarks, our CMNeXt achieves
state-of-the-art performance on the DeLiVER, KITTI-360, MFNet, NYU Depth V2,
UrbanLF, and MCubeS datasets, allowing to scale from 1 to 81 modalities. On the
freshly collected DeLiVER, the quad-modal CMNeXt reaches up to 66.30% in mIoU
with a +9.10% gain as compared to the mono-modal baseline. The DeLiVER dataset
and our code are at: https://jamycheung.github.io/DELIVER.html.
|
[
{
"version": "v1",
"created": "Thu, 2 Mar 2023 18:41:41 GMT"
}
] | 2023-03-03T00:00:00 |
[
[
"Zhang",
"Jiaming",
""
],
[
"Liu",
"Ruiping",
""
],
[
"Shi",
"Hao",
""
],
[
"Yang",
"Kailun",
""
],
[
"Reiß",
"Simon",
""
],
[
"Peng",
"Kunyu",
""
],
[
"Fu",
"Haodong",
""
],
[
"Wang",
"Kaiwei",
""
],
[
"Stiefelhagen",
"Rainer",
""
]
] |
new_dataset
| 0.988953 |
2203.09655
|
Jiehua Chen
|
Jiehua Chen and Gergely Cs\'aji and Sanjukta Roy and Sofia Simola
|
Hedonic Games With Friends, Enemies, and Neutrals: Resolving Open
Questions and Fine-Grained Complexity
|
extended abstract appeared at AAMAS 2023
| null | null | null |
cs.GT cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate verification and existence problems for prominent stability
concepts in hedonic games with friends, enemies, and optionally with neutrals
[8, 16]. We resolve several (long-standing) open questions [4, 16, 20, 23] and
show that for friend-oriented preferences, under the friends and enemies model,
it is coNP-complete to verify whether a given agent partition is (strictly)
core stable, while under the friends, enemies, and neutrals model, it is
NP-complete to determine whether an individual stable partition exists. We
further look into natural restricted cases from the literature, such as when
the friends and enemies relationships are symmetric, when the initial
coalitions have bounded size, when the vertex degree in the friendship graph
(resp. the union of friendship and enemy graph) is bounded, or when such graph
is acyclic or close to being acyclic. We obtain a complete (parameterized)
complexity picture regarding these cases.
|
[
{
"version": "v1",
"created": "Thu, 17 Mar 2022 23:31:48 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 02:56:48 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Chen",
"Jiehua",
""
],
[
"Csáji",
"Gergely",
""
],
[
"Roy",
"Sanjukta",
""
],
[
"Simola",
"Sofia",
""
]
] |
new_dataset
| 0.999819 |
2209.08196
|
Jordan Ford
|
Jeff Ford and Jordan Ford
|
Lossless SIMD Compression of LiDAR Range and Attribute Scan Sequences
| null | null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data
compression algorithm has increased. Modern LiDARs produce gigabytes of scan
data per hour and are often used in applications with limited compute,
bandwidth, and storage resources.
We present a fast, lossless compression algorithm for LiDAR range and
attribute scan sequences including multiple-return range, signal, reflectivity,
and ambient infrared. Our algorithm -- dubbed "Jiffy" -- achieves substantial
compression by exploiting spatiotemporal redundancy and sparsity. Speed is
accomplished by maximizing use of single-instruction-multiple-data (SIMD)
instructions. In autonomous driving, infrastructure monitoring, drone
inspection, and handheld mapping benchmarks, the Jiffy algorithm consistently
outcompresses competing lossless codecs while operating at speeds in excess of
65M points/sec on a single core. In a typical autonomous vehicle use case,
single-threaded Jiffy achieves 6x compression of centimeter-precision range
scans at 500+ scans per second. To ensure reproducibility and enable adoption,
the software is freely available as an open source library.
|
[
{
"version": "v1",
"created": "Fri, 16 Sep 2022 23:29:48 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 21:30:32 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Ford",
"Jeff",
""
],
[
"Ford",
"Jordan",
""
]
] |
new_dataset
| 0.999451 |
2209.09359
|
Onur Selim Kilic
|
Onur Selim K{\i}l{\i}\c{c}, Ahmet Akman and A. Ayd{\i}n Alatan
|
E-VFIA : Event-Based Video Frame Interpolation with Attention
|
Accepted to 2023 IEEE International Conference on Robotics and
Automation (ICRA 2023)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Video frame interpolation (VFI) is a fundamental vision task that aims to
synthesize several frames between two consecutive original video images. Most
algorithms aim to accomplish VFI by using only keyframes, which is an ill-posed
problem since the keyframes usually do not yield any accurate precision about
the trajectories of the objects in the scene. On the other hand, event-based
cameras provide more precise information between the keyframes of a video. Some
recent state-of-the-art event-based methods approach this problem by utilizing
event data for better optical flow estimation to interpolate for video frame by
warping. Nonetheless, those methods heavily suffer from the ghosting effect. On
the other hand, some of kernel-based VFI methods that only use frames as input,
have shown that deformable convolutions, when backed up with transformers, can
be a reliable way of dealing with long-range dependencies. We propose
event-based video frame interpolation with attention (E-VFIA), as a lightweight
kernel-based method. E-VFIA fuses event information with standard video frames
by deformable convolutions to generate high quality interpolated frames. The
proposed method represents events with high temporal resolution and uses a
multi-head self-attention mechanism to better encode event-based information,
while being less vulnerable to blurring and ghosting artifacts; thus,
generating crispier frames. The simulation results show that the proposed
technique outperforms current state-of-the-art methods (both frame and
event-based) with a significantly smaller model size.
|
[
{
"version": "v1",
"created": "Mon, 19 Sep 2022 21:40:32 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Feb 2023 22:10:17 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Mar 2023 12:52:16 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Kılıç",
"Onur Selim",
""
],
[
"Akman",
"Ahmet",
""
],
[
"Alatan",
"A. Aydın",
""
]
] |
new_dataset
| 0.992995 |
2209.13657
|
Neelay Joglekar
|
Neelay Joglekar, Fei Liu, Ryan Orosco, Michael Yip
|
Suture Thread Spline Reconstruction from Endoscopic Images for Robotic
Surgery with Reliability-driven Keypoint Detection
|
To be published in ICRA 2023
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automating the process of manipulating and delivering sutures during robotic
surgery is a prominent problem at the frontier of surgical robotics, as
automating this task can significantly reduce surgeons' fatigue during
tele-operated surgery and allow them to spend more time addressing higher-level
clinical decision making. Accomplishing autonomous suturing and suture
manipulation in the real world requires accurate suture thread localization and
reconstruction, the process of creating a 3D shape representation of suture
thread from 2D stereo camera surgical image pairs. This is a very challenging
problem due to how limited pixel information is available for the threads, as
well as their sensitivity to lighting and specular reflection. We present a
suture thread reconstruction work that uses reliable keypoints and a Minimum
Variation Spline (MVS) smoothing optimization to construct a 3D centerline from
a segmented surgical image pair. This method is comparable to previous suture
thread reconstruction works, with the possible benefit of increased accuracy of
grasping point estimation. Our code and datasets will be available at:
https://github.com/ucsdarclab/thread-reconstruction.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 19:48:20 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Oct 2022 10:12:01 GMT"
},
{
"version": "v3",
"created": "Tue, 28 Feb 2023 22:42:12 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Joglekar",
"Neelay",
""
],
[
"Liu",
"Fei",
""
],
[
"Orosco",
"Ryan",
""
],
[
"Yip",
"Michael",
""
]
] |
new_dataset
| 0.996334 |
2210.00120
|
Ruiqi Ni
|
Ruiqi Ni, Ahmed H. Qureshi
|
NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
| null | null | null | null |
cs.RO cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neural Motion Planners (NMPs) have emerged as a promising tool for solving
robot navigation tasks in complex environments. However, these methods often
require expert data for learning, which limits their application to scenarios
where data generation is time-consuming. Recent developments have also led to
physics-informed deep neural models capable of representing complex dynamical
Partial Differential Equations (PDEs). Inspired by these developments, we
propose Neural Time Fields (NTFields) for robot motion planning in cluttered
scenarios. Our framework represents a wave propagation model generating
continuous arrival time to find path solutions informed by a nonlinear
first-order PDE called Eikonal Equation. We evaluate our method in various
cluttered 3D environments, including the Gibson dataset, and demonstrate its
ability to solve motion planning problems for 4-DOF and 6-DOF robot
manipulators where the traditional grid-based Eikonal planners often face the
curse of dimensionality. Furthermore, the results show that our method exhibits
high success rates and significantly lower computational times than the
state-of-the-art methods, including NMPs that require training data from
classical planners.
|
[
{
"version": "v1",
"created": "Fri, 30 Sep 2022 22:34:54 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 15:23:49 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Ni",
"Ruiqi",
""
],
[
"Qureshi",
"Ahmed H.",
""
]
] |
new_dataset
| 0.994765 |
2210.00312
|
Ningyu Zhang
|
Ningyu Zhang, Lei Li, Xiang Chen, Xiaozhuan Liang, Shumin Deng, Huajun
Chen
|
Multimodal Analogical Reasoning over Knowledge Graphs
|
Accepted by ICLR 2023. The project website is
https://zjunlp.github.io/project/MKG_Analogy/introduction.html
| null | null | null |
cs.CL cs.AI cs.CV cs.LG cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Analogical reasoning is fundamental to human cognition and holds an important
place in various fields. However, previous studies mainly focus on single-modal
analogical reasoning and ignore taking advantage of structure knowledge.
Notably, the research in cognitive psychology has demonstrated that information
from multimodal sources always brings more powerful cognitive transfer than
single modality sources. To this end, we introduce the new task of multimodal
analogical reasoning over knowledge graphs, which requires multimodal reasoning
ability with the help of background knowledge. Specifically, we construct a
Multimodal Analogical Reasoning dataSet (MARS) and a multimodal knowledge graph
MarKG. We evaluate with multimodal knowledge graph embedding and pre-trained
Transformer baselines, illustrating the potential challenges of the proposed
task. We further propose a novel model-agnostic Multimodal analogical reasoning
framework with Transformer (MarT) motivated by the structure mapping theory,
which can obtain better performance. Code and datasets are available in
https://github.com/zjunlp/MKG_Analogy.
|
[
{
"version": "v1",
"created": "Sat, 1 Oct 2022 16:24:15 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Nov 2022 10:40:00 GMT"
},
{
"version": "v3",
"created": "Wed, 25 Jan 2023 05:26:39 GMT"
},
{
"version": "v4",
"created": "Wed, 1 Mar 2023 02:51:12 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Zhang",
"Ningyu",
""
],
[
"Li",
"Lei",
""
],
[
"Chen",
"Xiang",
""
],
[
"Liang",
"Xiaozhuan",
""
],
[
"Deng",
"Shumin",
""
],
[
"Chen",
"Huajun",
""
]
] |
new_dataset
| 0.998721 |
2210.10992
|
Zeyu Huang
|
Zeyu Huang, Juzhan Xu, Sisi Dai, Kai Xu, Hao Zhang, Hui Huang, Ruizhen
Hu
|
NIFT: Neural Interaction Field and Template for Object Manipulation
|
ICRA 2023
| null | null | null |
cs.RO cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce NIFT, Neural Interaction Field and Template, a descriptive and
robust interaction representation of object manipulations to facilitate
imitation learning. Given a few object manipulation demos, NIFT guides the
generation of the interaction imitation for a new object instance by matching
the Neural Interaction Template (NIT) extracted from the demos in the target
Neural Interaction Field (NIF) defined for the new object. Specifically, the
NIF is a neural field that encodes the relationship between each spatial point
and a given object, where the relative position is defined by a spherical
distance function rather than occupancies or signed distances, which are
commonly adopted by conventional neural fields but less informative. For a
given demo interaction, the corresponding NIT is defined by a set of spatial
points sampled in the demo NIF with associated neural features. To better
capture the interaction, the points are sampled on the Interaction Bisector
Surface (IBS), which consists of points that are equidistant to the two
interacting objects and has been used extensively for interaction
representation. With both point selection and pointwise features defined for
better interaction encoding, NIT effectively guides the feature matching in the
NIFs of the new object instances such that the relative poses are optimized to
realize the manipulation while imitating the demo interactions. Experiments
show that our NIFT solution outperforms state-of-the-art imitation learning
methods for object manipulation and generalizes better to objects from new
categories.
|
[
{
"version": "v1",
"created": "Thu, 20 Oct 2022 03:35:05 GMT"
},
{
"version": "v2",
"created": "Fri, 21 Oct 2022 01:56:47 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Mar 2023 01:30:41 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Huang",
"Zeyu",
""
],
[
"Xu",
"Juzhan",
""
],
[
"Dai",
"Sisi",
""
],
[
"Xu",
"Kai",
""
],
[
"Zhang",
"Hao",
""
],
[
"Huang",
"Hui",
""
],
[
"Hu",
"Ruizhen",
""
]
] |
new_dataset
| 0.988815 |
2212.10763
|
Zhiang Chen
|
Zhiang Chen, Devin Keating, Yash Shethwala, Aravind Adhith Pandian
Saravanakumaran, Ramon Arrowsmith, Albert Kottke, Christine Wittich,
Jnaneshwar Das
|
Shakebot: A Low-cost, Open-source Robotic Shake Table for Earthquake
Research and Education
| null | null | null | null |
cs.RO physics.geo-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Shake tables provide a critical tool for simulating earthquake events and
testing the response of structures to seismic forces. However, existing shake
tables are either expensive or proprietary. This paper presents the design and
implementation of a low-cost, open-source shake table named Shakebot for
earthquake engineering research and education, built using Robot Operating
System (ROS) and robotic concepts. The Shakebot adapts affordable and
high-accuracy components from 3D printers, particularly a closed-loop stepper
motor for actuation and a toothed belt for transmission. The stepper motor
enables the bed to reach a maximum horizontal acceleration of 11.8 m/s^2 (1.2
g), and velocity of 0.5 m/s, with a 2 kg specimen. The Shakebot is equipped
with an accelerometer and a high frame-rate camera for bed motion estimation.
The low cost and easy use make the Shakebot accessible to a wide range of
users, including students, educators, and researchers in low-resource settings.
An important application of the Shakebot is to examine the dynamics of
precariously balanced rocks (PBRs), which are negative indicators of
earthquakes in nature. Our earlier research built a virtual shake robot in
simulation for the PBR study. The Shakebot provides an approach to validate the
simulation through physical experiments. The ROS-based perception and motion
software facilitates the code transition from our virtual shake robot to the
physical Shakebot. The reuse of the control programs ensures that the
implemented ground motions are consistent for both the simulation and physical
experiments, which is critical to validate our simulation experiments.
|
[
{
"version": "v1",
"created": "Wed, 21 Dec 2022 04:49:46 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Feb 2023 22:53:47 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Mar 2023 03:59:13 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Chen",
"Zhiang",
""
],
[
"Keating",
"Devin",
""
],
[
"Shethwala",
"Yash",
""
],
[
"Saravanakumaran",
"Aravind Adhith Pandian",
""
],
[
"Arrowsmith",
"Ramon",
""
],
[
"Kottke",
"Albert",
""
],
[
"Wittich",
"Christine",
""
],
[
"Das",
"Jnaneshwar",
""
]
] |
new_dataset
| 0.999791 |
2301.07425
|
Pengyu Yin
|
Pengyu Yin, Shenghai Yuan, Haozhi Cao, Xingyu Ji, Shuyang Zhang, and
Lihua Xie
|
Segregator: Global Point Cloud Registration with Semantic and Geometric
Cues
|
6 pages, 5 figures. Accepted to ICRA2023
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents Segregator, a global point cloud registration framework
that exploits both semantic information and geometric distribution to
efficiently build up outlier-robust correspondences and search for inliers.
Current state-of-the-art algorithms rely on point features to set up putative
correspondences and refine them by employing pair-wise distance consistency
checks. However, such a scheme suffers from degenerate cases, where the
descriptive capability of local point features downgrades, and unconstrained
cases, where length-preserving (l-TRIMs)-based checks cannot sufficiently
constrain whether the current observation is consistent with others, resulting
in a complexified NP-complete problem to solve. To tackle these problems, on
the one hand, we propose a novel degeneracy-robust and efficient corresponding
procedure consisting of both instance-level semantic clusters and
geometric-level point features. On the other hand, Gaussian distribution-based
translation and rotation invariant measurements (G-TRIMs) are proposed to
conduct the consistency check and further constrain the problem size. We
validated our proposed algorithm on extensive real-world data-based
experiments. The code is available: https://github.com/Pamphlett/Segregator.
|
[
{
"version": "v1",
"created": "Wed, 18 Jan 2023 10:47:45 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 02:10:45 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Yin",
"Pengyu",
""
],
[
"Yuan",
"Shenghai",
""
],
[
"Cao",
"Haozhi",
""
],
[
"Ji",
"Xingyu",
""
],
[
"Zhang",
"Shuyang",
""
],
[
"Xie",
"Lihua",
""
]
] |
new_dataset
| 0.955307 |
2301.12711
|
Elmurod Kuriyozov
|
Maksud Sharipov, Elmurod Kuriyozov, Ollabergan Yuldashev, Ogabek
Sobirov
|
UzbekTagger: The rule-based POS tagger for Uzbek language
|
Preprint of the accepted paper to The 10th Language & Technology
Conference: Human Language Technologies as a Challenge for Computer Science
and Linguistics, April 21-23, 2023, Poznan, Poland
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
This research paper presents a part-of-speech (POS) annotated dataset and
tagger tool for the low-resource Uzbek language. The dataset includes 12 tags,
which were used to develop a rule-based POS-tagger tool. The corpus text used
in the annotation process was made sure to be balanced over 20 different fields
in order to ensure its representativeness. Uzbek being an agglutinative
language so the most of the words in an Uzbek sentence are formed by adding
suffixes. This nature of it makes the POS-tagging task difficult to find the
stems of words and the right part-of-speech they belong to. The methodology
proposed in this research is the stemming of the words with an affix/suffix
stripping approach including database of the stem forms of the words in the
Uzbek language. The tagger tool was tested on the annotated dataset and showed
high accuracy in identifying and tagging parts of speech in Uzbek text. This
newly presented dataset and tagger tool can be used for a variety of natural
language processing tasks such as language modeling, machine translation, and
text-to-speech synthesis. The presented dataset is the first of its kind to be
made publicly available for Uzbek, and the POS-tagger tool created can also be
used as a pivot to use as a base for other closely-related Turkic languages.
|
[
{
"version": "v1",
"created": "Mon, 30 Jan 2023 07:40:45 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 14:31:12 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Sharipov",
"Maksud",
""
],
[
"Kuriyozov",
"Elmurod",
""
],
[
"Yuldashev",
"Ollabergan",
""
],
[
"Sobirov",
"Ogabek",
""
]
] |
new_dataset
| 0.999612 |
2302.06932
|
Richard Mitev
|
Marvin Sa{\ss}, Richard Mitev, Ahmad-Reza Sadeghi
|
Oops..! I Glitched It Again! How to Multi-Glitch the
Glitching-Protections on ARM TrustZone-M
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Voltage Fault Injection (VFI), also known as power glitching, has proven to
be a severe threat to real-world systems. In VFI attacks, the adversary
disturbs the power-supply of the target-device forcing the device to
illegitimate behavior. Various countermeasures have been proposed to address
different types of fault injection attacks at different abstraction layers,
either requiring to modify the underlying hardware or software/firmware at the
machine instruction level. Moreover, only recently, individual chip
manufacturers have started to respond to this threat by integrating
countermeasures in their products. Generally, these countermeasures aim at
protecting against single fault injection (SFI) attacks, since Multiple Fault
Injection (MFI) is believed to be challenging and sometimes even impractical.
In this paper, we present {\mu}-Glitch, the first Voltage Fault Injection (VFI)
platform which is capable of injecting multiple, coordinated voltage faults
into a target device, requiring only a single trigger signal. We provide a
novel flow for Multiple Voltage Fault Injection (MVFI) attacks to significantly
reduce the search complexity for fault parameters, as the search space
increases exponentially with each additional fault injection. We evaluate and
showcase the effectiveness and practicality of our attack platform on four
real-world chips, featuring TrustZone-M: The first two have interdependent
backchecking mechanisms, while the second two have additionally integrated
countermeasures against fault injection. Our evaluation revealed that
{\mu}-Glitch can successfully inject four consecutive faults within an average
time of one day. Finally, we discuss potential countermeasures to mitigate VFI
attacks and additionally propose two novel attack scenarios for MVFI.
|
[
{
"version": "v1",
"created": "Tue, 14 Feb 2023 09:40:09 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 08:13:42 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Saß",
"Marvin",
""
],
[
"Mitev",
"Richard",
""
],
[
"Sadeghi",
"Ahmad-Reza",
""
]
] |
new_dataset
| 0.992663 |
2302.12840
|
Isabel Segura-Bedmar
|
Isabel Segura-Bedmar
|
HULAT at SemEval-2023 Task 10: Data augmentation for pre-trained
transformers applied to the detection of sexism in social media
|
The experiments are not reproducible because I did not use a seed for
replicability
| null | null | null |
cs.CL cs.AI cs.LG cs.NE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper describes our participation in SemEval-2023 Task 10, whose goal is
the detection of sexism in social media. We explore some of the most popular
transformer models such as BERT, DistilBERT, RoBERTa, and XLNet. We also study
different data augmentation techniques to increase the training dataset. During
the development phase, our best results were obtained by using RoBERTa and data
augmentation for tasks B and C. However, the use of synthetic data does not
improve the results for task C. We participated in the three subtasks. Our
approach still has much room for improvement, especially in the two
fine-grained classifications. All our code is available in the repository
https://github.com/isegura/hulat_edos.
|
[
{
"version": "v1",
"created": "Fri, 24 Feb 2023 18:17:38 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 08:43:13 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Segura-Bedmar",
"Isabel",
""
]
] |
new_dataset
| 0.995826 |
2302.13838
|
Naoya Takahashi
|
Naoya Takahashi, Mayank K. Singh, Yuki Mitsufuji
|
Cross-modal Face- and Voice-style Transfer
| null | null | null | null |
cs.CV cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Image-to-image translation and voice conversion enable the generation of a
new facial image and voice while maintaining some of the semantics such as a
pose in an image and linguistic content in audio, respectively. They can aid in
the content-creation process in many applications. However, as they are limited
to the conversion within each modality, matching the impression of the
generated face and voice remains an open question. We propose a cross-modal
style transfer framework called XFaVoT that jointly learns four tasks: image
translation and voice conversion tasks with audio or image guidance, which
enables the generation of ``face that matches given voice" and ``voice that
matches given face", and intra-modality translation tasks with a single
framework. Experimental results on multiple datasets show that XFaVoT achieves
cross-modal style translation of image and voice, outperforming baselines in
terms of quality, diversity, and face-voice correspondence.
|
[
{
"version": "v1",
"created": "Mon, 27 Feb 2023 14:39:50 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 14:50:41 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Takahashi",
"Naoya",
""
],
[
"Singh",
"Mayank K.",
""
],
[
"Mitsufuji",
"Yuki",
""
]
] |
new_dataset
| 0.958537 |
2302.14340
|
Zhihao Liang
|
Zhihao Liang, Zhangjin Huang, Changxing Ding, Kui Jia
|
HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of
Indoor Scenes with Iterative Intertwined Regularization
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recovery of an underlying scene geometry from multiview images stands as a
long-time challenge in computer vision research. The recent promise leverages
neural implicit surface learning and differentiable volume rendering, and
achieves both the recovery of scene geometry and synthesis of novel views,
where deep priors of neural models are used as an inductive smoothness bias.
While promising for object-level surfaces, these methods suffer when coping
with complex scene surfaces. In the meanwhile, traditional multi-view stereo
can recover the geometry of scenes with rich textures, by globally optimizing
the local, pixel-wise correspondences across multiple views. We are thus
motivated to make use of the complementary benefits from the two strategies,
and propose a method termed Helix-shaped neural implicit Surface learning or
HelixSurf; HelixSurf uses the intermediate prediction from one strategy as the
guidance to regularize the learning of the other one, and conducts such
intertwined regularization iteratively during the learning process. We also
propose an efficient scheme for differentiable volume rendering in HelixSurf.
Experiments on surface reconstruction of indoor scenes show that our method
compares favorably with existing methods and is orders of magnitude faster,
even when some of existing methods are assisted with auxiliary training data.
The source code is available at https://github.com/Gorilla-Lab-SCUT/HelixSurf.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 06:20:07 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 12:24:02 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Liang",
"Zhihao",
""
],
[
"Huang",
"Zhangjin",
""
],
[
"Ding",
"Changxing",
""
],
[
"Jia",
"Kui",
""
]
] |
new_dataset
| 0.993082 |
2303.00050
|
Decai Chen
|
Decai Chen, Haofei Lu, Ingo Feldmann, Oliver Schreer, Peter Eisert
|
Dynamic Multi-View Scene Reconstruction Using Neural Implicit Surface
|
5 pages, accepted by ICASSP 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Reconstructing general dynamic scenes is important for many computer vision
and graphics applications. Recent works represent the dynamic scene with neural
radiance fields for photorealistic view synthesis, while their surface geometry
is under-constrained and noisy. Other works introduce surface constraints to
the implicit neural representation to disentangle the ambiguity of geometry and
appearance field for static scene reconstruction. To bridge the gap between
rendering dynamic scenes and recovering static surface geometry, we propose a
template-free method to reconstruct surface geometry and appearance using
neural implicit representations from multi-view videos. We leverage
topology-aware deformation and the signed distance field to learn complex
dynamic surfaces via differentiable volume rendering without scene-specific
prior knowledge like template models. Furthermore, we propose a novel
mask-based ray selection strategy to significantly boost the optimization on
challenging time-varying regions. Experiments on different multi-view video
datasets demonstrate that our method achieves high-fidelity surface
reconstruction as well as photorealistic novel view synthesis.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 19:47:30 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Chen",
"Decai",
""
],
[
"Lu",
"Haofei",
""
],
[
"Feldmann",
"Ingo",
""
],
[
"Schreer",
"Oliver",
""
],
[
"Eisert",
"Peter",
""
]
] |
new_dataset
| 0.978099 |
2303.00064
|
Richard Van Dijk
|
Richard van Dijk, Daniela Gawehns and Matthijs van Leeuwen
|
WEARDA: recording wearable sensor data for human activity monitoring
|
Submitted to the Journal of Open Research Software JORS, Jan 19th,
2023, 17 pages, 5 figures, 3 tables
| null | null | null |
cs.HC cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
We present WEARDA, the open source WEARable sensor Data Acquisition software
package. WEARDA facilitates the acquisition of human activity data with
smartwatches and is primarily aimed at researchers who require transparency,
full control, and access to raw sensor data. It provides functionality to
simultaneously record raw data from four sensors -- tri-axis accelerometer,
tri-axis gyroscope, barometer, and GPS -- which should enable researchers to,
for example, estimate energy expenditure and mine movement trajectories. A
Samsung smartwatch running the Tizen OS was chosen because of 1) the required
functionalities of the smartwatch software API, 2) the availability of software
development tools and accessible documentation, 3) having the required sensors,
and 4) the requirements on case design for acceptance by the target user group.
WEARDA addresses five practical challenges concerning preparation, measurement,
logistics, privacy preservation, and reproducibility to ensure efficient and
errorless data collection. The software package was initially created for the
project ``Dementia back at the heart of the community'', and has been
successfully used in that context.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 20:07:46 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"van Dijk",
"Richard",
""
],
[
"Gawehns",
"Daniela",
""
],
[
"van Leeuwen",
"Matthijs",
""
]
] |
new_dataset
| 0.999498 |
2303.00069
|
Ajinkya Kulkarni
|
Ajinkya Kulkarni and Atharva Kulkarni and Sara Abedalmonem Mohammad
Shatnawi and Hanan Aldarmaki
|
ClArTTS: An Open-Source Classical Arabic Text-to-Speech Corpus
|
None
| null | null | null |
cs.CL cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
At present, Text-to-speech (TTS) systems that are trained with high-quality
transcribed speech data using end-to-end neural models can generate speech that
is intelligible, natural, and closely resembles human speech. These models are
trained with relatively large single-speaker professionally recorded audio,
typically extracted from audiobooks. Meanwhile, due to the scarcity of freely
available speech corpora of this kind, a larger gap exists in Arabic TTS
research and development. Most of the existing freely available Arabic speech
corpora are not suitable for TTS training as they contain multi-speaker casual
speech with variations in recording conditions and quality, whereas the corpus
curated for speech synthesis are generally small in size and not suitable for
training state-of-the-art end-to-end models. In a move towards filling this gap
in resources, we present a speech corpus for Classical Arabic Text-to-Speech
(ClArTTS) to support the development of end-to-end TTS systems for Arabic. The
speech is extracted from a LibriVox audiobook, which is then processed,
segmented, and manually transcribed and annotated. The final ClArTTS corpus
contains about 12 hours of speech from a single male speaker sampled at 40100
kHz. In this paper, we describe the process of corpus creation and provide
details of corpus statistics and a comparison with existing resources.
Furthermore, we develop two TTS systems based on Grad-TTS and Glow-TTS and
illustrate the performance of the resulting systems via subjective and
objective evaluations. The corpus will be made publicly available at
www.clartts.com for research purposes, along with the baseline TTS systems
demo.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 20:18:59 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Kulkarni",
"Ajinkya",
""
],
[
"Kulkarni",
"Atharva",
""
],
[
"Shatnawi",
"Sara Abedalmonem Mohammad",
""
],
[
"Aldarmaki",
"Hanan",
""
]
] |
new_dataset
| 0.999879 |
2303.00137
|
Yichen Sheng
|
Yichen Sheng, Jianming Zhang, Julien Philip, Yannick Hold-Geoffroy,
Xin Sun, HE Zhang, Lu Ling, Bedrich Benes
|
PixHt-Lab: Pixel Height Based Light Effect Generation for Image
Compositing
|
11 pages, 10 figures
| null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Lighting effects such as shadows or reflections are key in making synthetic
images realistic and visually appealing. To generate such effects, traditional
computer graphics uses a physically-based renderer along with 3D geometry. To
compensate for the lack of geometry in 2D Image compositing, recent deep
learning-based approaches introduced a pixel height representation to generate
soft shadows and reflections. However, the lack of geometry limits the quality
of the generated soft shadows and constrain reflections to pure specular ones.
We introduce PixHt-Lab, a system leveraging an explicit mapping from pixel
height representation to 3D space. Using this mapping, PixHt-Lab reconstructs
both the cutout and background geometry and renders realistic, diverse,
lighting effects for image compositing. Given a surface with physically-based
materials, we can render reflections with varying glossiness. To generate more
realistic soft shadows, we further propose to use 3D-aware buffer channels to
guide a neural renderer. Both quantitative and qualitative evaluations
demonstrate that PixHt-Lab significantly improves soft shadow generation.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 23:52:01 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Sheng",
"Yichen",
""
],
[
"Zhang",
"Jianming",
""
],
[
"Philip",
"Julien",
""
],
[
"Hold-Geoffroy",
"Yannick",
""
],
[
"Sun",
"Xin",
""
],
[
"Zhang",
"HE",
""
],
[
"Ling",
"Lu",
""
],
[
"Benes",
"Bedrich",
""
]
] |
new_dataset
| 0.997116 |
2303.00152
|
Franck Cassez
|
Franck Cassez, Joanne Fuller, Milad K. Ghale, David J. Pearce, and
Horacio M. A. Quiles
|
Formal and Executable Semantics of the Ethereum Virtual Machine in Dafny
| null | null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
The Ethereum protocol implements a replicated state machine. The network
participants keep track of the system state by: 1) agreeing on the sequence of
transactions to be processed and 2) computing the state transitions that
correspond to the sequence of transactions. Ethereum transactions are programs,
called smart contracts, and computing a state transition requires executing
some code. The Ethereum Virtual Machine (EVM) provides this capability and can
execute programs written in EVM bytecode. We present a formal and executable
semantics of the EVM written in the verification-friendly language Dafny: it
provides (i) a readable, formal and verified specification of the semantics of
the EVM; (ii) a framework to formally reason about bytecode.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 00:55:33 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Cassez",
"Franck",
""
],
[
"Fuller",
"Joanne",
""
],
[
"Ghale",
"Milad K.",
""
],
[
"Pearce",
"David J.",
""
],
[
"Quiles",
"Horacio M. A.",
""
]
] |
new_dataset
| 0.998535 |
2303.00168
|
Jingsen Zhang
|
Xu Chen, Jingsen Zhang, Lei Wang, Quanyu Dai, Zhenhua Dong, Ruiming
Tang, Rui Zhang, Li Chen, Ji-Rong Wen
|
REASONER: An Explainable Recommendation Dataset with Multi-aspect Real
User Labeled Ground Truths Towards more Measurable Explainable Recommendation
| null | null | null | null |
cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Explainable recommendation has attracted much attention from the industry and
academic communities. It has shown great potential for improving the
recommendation persuasiveness, informativeness and user satisfaction. Despite a
lot of promising explainable recommender models have been proposed in the past
few years, the evaluation strategies of these models suffer from several
limitations. For example, the explanation ground truths are not labeled by real
users, the explanations are mostly evaluated based on only one aspect and the
evaluation strategies can be hard to unify. To alleviate the above problems, we
propose to build an explainable recommendation dataset with multi-aspect real
user labeled ground truths. In specific, we firstly develop a video
recommendation platform, where a series of questions around the recommendation
explainability are carefully designed. Then, we recruit about 3000 users with
different backgrounds to use the system, and collect their behaviors and
feedback to our questions. In this paper, we detail the construction process of
our dataset and also provide extensive analysis on its characteristics. In
addition, we develop a library, where ten well-known explainable recommender
models are implemented in a unified framework. Based on this library, we build
several benchmarks for different explainable recommendation tasks. At last, we
present many new opportunities brought by our dataset, which are expected to
shed some new lights to the explainable recommendation field. Our dataset,
library and the related documents have been released at
https://reasoner2023.github.io/.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 01:46:52 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Chen",
"Xu",
""
],
[
"Zhang",
"Jingsen",
""
],
[
"Wang",
"Lei",
""
],
[
"Dai",
"Quanyu",
""
],
[
"Dong",
"Zhenhua",
""
],
[
"Tang",
"Ruiming",
""
],
[
"Zhang",
"Rui",
""
],
[
"Chen",
"Li",
""
],
[
"Wen",
"Ji-Rong",
""
]
] |
new_dataset
| 0.994426 |
2303.00171
|
Raviteja Anantha
|
Raviteja Anantha, Kriti Bhasin, Daniela de la Parra Aguilar, Prabal
Vashisht, Becci Williamson, Srinivas Chappidi
|
DTW-SiameseNet: Dynamic Time Warped Siamese Network for Mispronunciation
Detection and Correction
|
Preprint version
| null | null | null |
cs.LG cs.AI eess.AS
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Personal Digital Assistants (PDAs) - such as Siri, Alexa and Google
Assistant, to name a few - play an increasingly important role to access
information and complete tasks spanning multiple domains, and by diverse groups
of users. A text-to-speech (TTS) module allows PDAs to interact in a natural,
human-like manner, and play a vital role when the interaction involves people
with visual impairments or other disabilities. To cater to the needs of a
diverse set of users, inclusive TTS is important to recognize and pronounce
correctly text in different languages and dialects. Despite great progress in
speech synthesis, the pronunciation accuracy of named entities in a
multi-lingual setting still has a large room for improvement. Existing
approaches to correct named entity (NE) mispronunciations, like retraining
Grapheme-to-Phoneme (G2P) models, or maintaining a TTS pronunciation
dictionary, require expensive annotation of the ground truth pronunciation,
which is also time consuming. In this work, we present a highly-precise,
PDA-compatible pronunciation learning framework for the task of TTS
mispronunciation detection and correction. In addition, we also propose a novel
mispronunciation detection model called DTW-SiameseNet, which employs metric
learning with a Siamese architecture for Dynamic Time Warping (DTW) with
triplet loss. We demonstrate that a locale-agnostic, privacy-preserving
solution to the problem of TTS mispronunciation detection is feasible. We
evaluate our approach on a real-world dataset, and a corpus of NE
pronunciations of an anonymized audio dataset of person names recorded by
participants from 10 different locales. Human evaluation shows our proposed
approach improves pronunciation accuracy on average by ~6% compared to strong
phoneme-based and audio-based baselines.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 01:53:11 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Anantha",
"Raviteja",
""
],
[
"Bhasin",
"Kriti",
""
],
[
"Aguilar",
"Daniela de la Parra",
""
],
[
"Vashisht",
"Prabal",
""
],
[
"Williamson",
"Becci",
""
],
[
"Chappidi",
"Srinivas",
""
]
] |
new_dataset
| 0.999631 |
2303.00193
|
Hanting Li
|
Hanting Li, Hongjing Niu, Zhaoqing Zhu, and Feng Zhao
|
CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial
Expression Recognition
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Facial expression recognition (FER) is an essential task for understanding
human behaviors. As one of the most informative behaviors of humans, facial
expressions are often compound and variable, which is manifested by the fact
that different people may express the same expression in very different ways.
However, most FER methods still use one-hot or soft labels as the supervision,
which lack sufficient semantic descriptions of facial expressions and are less
interpretable. Recently, contrastive vision-language pre-training (VLP) models
(e.g., CLIP) use text as supervision and have injected new vitality into
various computer vision tasks, benefiting from the rich semantics in text.
Therefore, in this work, we propose CLIPER, a unified framework for both static
and dynamic facial Expression Recognition based on CLIP. Besides, we introduce
multiple expression text descriptors (METD) to learn fine-grained expression
representations that make CLIPER more interpretable. We conduct extensive
experiments on several popular FER benchmarks and achieve state-of-the-art
performance, which demonstrates the effectiveness of CLIPER.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 02:59:55 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Li",
"Hanting",
""
],
[
"Niu",
"Hongjing",
""
],
[
"Zhu",
"Zhaoqing",
""
],
[
"Zhao",
"Feng",
""
]
] |
new_dataset
| 0.984459 |
2303.00204
|
Zhenduo Zhao
|
Zhenduo Zhao, Zhuo Li, Wenchao Wang, Pengyuan Zhang
|
PCF: ECAPA-TDNN with Progressive Channel Fusion for Speaker Verification
|
Accepted by ICASSP 2023
| null | null | null |
cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
ECAPA-TDNN is currently the most popular TDNN-series model for speaker
verification, which refreshed the state-of-the-art(SOTA) performance of TDNN
models. However, one-dimensional convolution has a global receptive field over
the feature channel. It destroys the time-frequency relevance of the
spectrogram. Besides, as ECAPA-TDNN only has five layers, a much shallower
structure compared to ResNet restricts the capability to generate deep
representations. To further improve ECAPA-TDNN, we propose a progressive
channel fusion strategy that splits the spectrogram across the feature channel
and gradually expands the receptive field through the network. Secondly, we
enlarge the model by extending the depth and adding branches. Our proposed
model achieves EER with 0.718 and minDCF(0.01) with 0.0858 on vox1o, relatively
improved 16.1\% and 19.5\% compared with ECAPA-TDNN-large.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 03:12:28 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Zhao",
"Zhenduo",
""
],
[
"Li",
"Zhuo",
""
],
[
"Wang",
"Wenchao",
""
],
[
"Zhang",
"Pengyuan",
""
]
] |
new_dataset
| 0.994437 |
2303.00207
|
Anna Karanika
|
Anna Karanika, Rui Yang, Xiaojuan Ma, Jiangran Wang, Shalni Sundram
and Indranil Gupta
|
CoMesh: Fully-Decentralized Control for Sense-Trigger-Actuate Routines
in Edge Meshes
|
12 pages, 12 figures
| null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While mesh networking for edge settings (e.g., smart buildings, farms,
battlefields, etc.) has received much attention, the layer of control over such
meshes remains largely centralized and cloud-based. This paper focuses on
applications with sense-trigger-actuate (STA) workloads -- these are similar to
the abstraction of routines popular in smart homes, but applied to larger-scale
edge IoT deployments. We present CoMesh, which tackles the challenge of
building local, non-cloud, and decentralized solutions for control of
sense-trigger-actuate applications. At its core CoMesh uses an abstraction
called k-groups to spread in a fine-grained way, the load of STA actions.
Coordination within the k-group uses selective fast and cheap mechanisms rather
than expensive off-the-shelf solutions. k-group selection is proactively
dynamic, and occurs by using a combination of zero-message-exchange mechanisms
(to reduce load) and locality sensitive hashing (to be aware of physical layout
of devices). We analyze and theoretically prove the safety of CoMesh's
mechanisms. Our evaluations using both simulation and Raspberry Pi lab
deployments show that CoMesh is load-balanced, fast, and fault-tolerant.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 03:18:43 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Karanika",
"Anna",
""
],
[
"Yang",
"Rui",
""
],
[
"Ma",
"Xiaojuan",
""
],
[
"Wang",
"Jiangran",
""
],
[
"Sundram",
"Shalni",
""
],
[
"Gupta",
"Indranil",
""
]
] |
new_dataset
| 0.991736 |
2303.00235
|
Yun Fan
|
Yun Fan, Yue Leng
|
Consta-dihedral Codes over Finite Fields
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It is proved in a reference (Fan, Lin, IEEE TIT, vol.67, pp.5016-5025) that
the self-dual (LCD respectively) dihedral codes over a finite field~$F$ with
${|F|=q}$ are asymptotically good if $q$ is even (odd respectively). In this
paper, we investigate the algebraic property and the asymptotic property of
conta-dihedral codes over $F$, and show that: if $q$ is even or $4\,|\,(q-1)$,
then the self-dual consta-dihedral codes are asymptotically good; otherwise,
the LCD consta-dihedral codes are asymptotically good. And, with the help of a
technique developed in this paper, some errors in the reference mentioned above
are corrected.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 05:04:40 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Fan",
"Yun",
""
],
[
"Leng",
"Yue",
""
]
] |
new_dataset
| 0.998117 |
2303.00260
|
Abhishek Verma
|
Sachin Kumar Verma, Abhishek Verma, Avinash Chandra Pandey
|
Addressing DAO Insider Attacks in IPv6-Based Low-Power and Lossy
Networks
| null |
In 2022 IEEE Region 10 Symposium (TENSYMP) (pp. 1-6). IEEE (July,
2022)
|
10.1109/TENSYMP54529.2022.9864545
| null |
cs.CR cs.NI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Low-Power and Lossy Networks (LLNs) run on resource-constrained devices and
play a key role in many Industrial Internet of Things and Cyber-Physical
Systems based applications. But, achieving an energy-efficient routing in LLNs
is a major challenge nowadays. This challenge is addressed by Routing Protocol
for Low-power Lossy Networks (RPL), which is specified in RFC 6550 as a
"Proposed Standard" at present. In RPL, a client node uses Destination
Advertisement Object (DAO) control messages to pass on the destination
information towards the root node. An attacker may exploit the DAO sending
mechanism of RPL to perform a DAO Insider attack in LLNs. In this paper, it is
shown that an aggressive attacker can drastically degrade the network
performance. To address DAO Insider attack, a lightweight defense solution is
proposed. The proposed solution uses an early blacklisting strategy to
significantly mitigate the attack and restore RPL performance. The proposed
solution is implemented and tested on Cooja Simulator.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 06:33:29 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Verma",
"Sachin Kumar",
""
],
[
"Verma",
"Abhishek",
""
],
[
"Pandey",
"Avinash Chandra",
""
]
] |
new_dataset
| 0.997358 |
2303.00300
|
Mingming Zhang
|
Mingming Zhang, Ye Du, Zhenghui Hu, Qingjie Liu, Yunhong Wang
|
BiSVP: Building Footprint Extraction via Bidirectional Serialized Vertex
Prediction
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Extracting building footprints from remote sensing images has been attracting
extensive attention recently. Dominant approaches address this challenging
problem by generating vectorized building masks with cumbersome refinement
stages, which limits the application of such methods. In this paper, we
introduce a new refinement-free and end-to-end building footprint extraction
method, which is conceptually intuitive, simple, and effective. Our method,
termed as BiSVP, represents a building instance with ordered vertices and
formulates the building footprint extraction as predicting the serialized
vertices directly in a bidirectional fashion. Moreover, we propose a
cross-scale feature fusion (CSFF) module to facilitate high resolution and rich
semantic feature learning, which is essential for the dense building vertex
prediction task. Without bells and whistles, our BiSVP outperforms
state-of-the-art methods by considerable margins on three building instance
segmentation benchmarks, clearly demonstrating its superiority. The code and
datasets will be made public available.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 07:50:34 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Zhang",
"Mingming",
""
],
[
"Du",
"Ye",
""
],
[
"Hu",
"Zhenghui",
""
],
[
"Liu",
"Qingjie",
""
],
[
"Wang",
"Yunhong",
""
]
] |
new_dataset
| 0.998812 |
2303.00322
|
Igor Sedl\'ar
|
Igor Sedl\'ar
|
Kleene Algebra With Tests for Weighted Programs
|
Full version of a paper accepted to ISMVL 2023
| null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Weighted programs generalize probabilistic programs and offer a framework for
specifying and encoding mathematical models by means of an algorithmic
representation. Kleene algebra with tests is an algebraic formalism based on
regular expressions with applications in proving program equivalence. We extend
the language of Kleene algebra with tests so that it is sufficient to formalize
reasoning about a simplified version weighted programs. We introduce relational
semantics for the extended language, and we generalize the relational semantics
to an appropriate extension of Kleene algebra with tests, called Kleene algebra
with weights and tests. We demonstrate by means of an example that Kleene
algebra with weights and tests offers a simple algebraic framework for
reasoning about equivalence and optimal runs of weighted programs.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 08:35:56 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Sedlár",
"Igor",
""
]
] |
new_dataset
| 0.990512 |
2303.00328
|
Luca Ferrarini
|
Yuri Faenza, Luca Ferrarini
|
The Total Matching Polytope of Complete Bipartite Graphs
|
17 pages
| null | null | null |
cs.DM math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The total matching polytope generalizes the stable set polytope and the
matching polytope. In this paper, we first propose new facet-defining
inequalities for the total matching polytope. We then give an
exponential-sized, non-redundant description in the original space and a
compact description in an extended space of the total matching polytope of
complete bipartite graphs.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 08:45:36 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Faenza",
"Yuri",
""
],
[
"Ferrarini",
"Luca",
""
]
] |
new_dataset
| 0.986341 |
2303.00337
|
Bilel Benjdira Dr.
|
Bilel Benjdira, Anis Koubaa, Ahmad Taher Azar, Zahid Khan, Adel Ammar,
Wadii Boulila
|
TAU: A Framework for Video-Based Traffic Analytics Leveraging Artificial
Intelligence and Unmanned Aerial Systems
|
This is the final proofread version submitted to Elsevier EAAI:
please see the published version at:
https://doi.org/10.1016/j.engappai.2022.105095
|
Engineering Applications of Artificial Intelligence, Volume 114,
2022, 105095, ISSN 0952-1976
|
10.1016/j.engappai.2022.105095
| null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Smart traffic engineering and intelligent transportation services are in
increasing demand from governmental authorities to optimize traffic performance
and thus reduce energy costs, increase the drivers' safety and comfort, ensure
traffic laws enforcement, and detect traffic violations. In this paper, we
address this challenge, and we leverage the use of Artificial Intelligence (AI)
and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics
framework, called TAU (Traffic Analysis from UAVs), for automated traffic
analytics and understanding. Unlike previous works on traffic video analytics,
we propose an automated object detection and tracking pipeline from video
processing to advanced traffic understanding using high-resolution UAV images.
TAU combines six main contributions. First, it proposes a pre-processing
algorithm to adapt the high-resolution UAV image as input to the object
detector without lowering the resolution. This ensures an excellent detection
accuracy from high-quality features, particularly the small size of detected
objects from UAV images. Second, it introduces an algorithm for recalibrating
the vehicle coordinates to ensure that vehicles are uniquely identified and
tracked across the multiple crops of the same frame. Third, it presents a speed
calculation algorithm based on accumulating information from successive frames.
Fourth, TAU counts the number of vehicles per traffic zone based on the Ray
Tracing algorithm. Fifth, TAU has a fully independent algorithm for crossroad
arbitration based on the data gathered from the different zones surrounding it.
Sixth, TAU introduces a set of algorithms for extracting twenty-four types of
insights from the raw data collected. The code is shared here:
https://github.com/bilel-bj/TAU. Video demonstrations are provided here:
https://youtu.be/wXJV0H7LviU and here: https://youtu.be/kGv0gmtVEbI.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 09:03:44 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Benjdira",
"Bilel",
""
],
[
"Koubaa",
"Anis",
""
],
[
"Azar",
"Ahmad Taher",
""
],
[
"Khan",
"Zahid",
""
],
[
"Ammar",
"Adel",
""
],
[
"Boulila",
"Wadii",
""
]
] |
new_dataset
| 0.988837 |
2303.00344
|
Yash Kumar Atri
|
Priyanshi Gupta, Yash Kumar Atri, Apurva Nagvenkar, Sourish Dasgupta,
Tanmoy Chakraborty
|
Inline Citation Classification using Peripheral Context and
Time-evolving Augmentation
|
accepted to PAKDD 2023
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Citation plays a pivotal role in determining the associations among research
articles. It portrays essential information in indicative, supportive, or
contrastive studies. The task of inline citation classification aids in
extrapolating these relationships; However, existing studies are still immature
and demand further scrutiny. Current datasets and methods used for inline
citation classification only use citation-marked sentences constraining the
model to turn a blind eye to domain knowledge and neighboring contextual
sentences. In this paper, we propose a new dataset, named 3Cext, which along
with the cited sentences, provides discourse information using the vicinal
sentences to analyze the contrasting and entailing relationships as well as
domain information. We propose PeriCite, a Transformer-based deep neural
network that fuses peripheral sentences and domain knowledge. Our model
achieves the state-of-the-art on the 3Cext dataset by +0.09 F1 against the best
baseline. We conduct extensive ablations to analyze the efficacy of the
proposed dataset and model fusion methods.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 09:11:07 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Gupta",
"Priyanshi",
""
],
[
"Atri",
"Yash Kumar",
""
],
[
"Nagvenkar",
"Apurva",
""
],
[
"Dasgupta",
"Sourish",
""
],
[
"Chakraborty",
"Tanmoy",
""
]
] |
new_dataset
| 0.995993 |
2303.00355
|
Liu Chenyang
|
Chenyang Liu, Jiajun Yang, Zipeng Qi, Zhengxia Zou and Zhenwei Shi
|
Progressive Scale-aware Network for Remote sensing Image Change
Captioning
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Remote sensing (RS) images contain numerous objects of different scales,
which poses significant challenges for the RS image change captioning (RSICC)
task to identify visual changes of interest in complex scenes and describe them
via language. However, current methods still have some weaknesses in
sufficiently extracting and utilizing multi-scale information. In this paper,
we propose a progressive scale-aware network (PSNet) to address the problem.
PSNet is a pure Transformer-based model. To sufficiently extract multi-scale
visual features, multiple progressive difference perception (PDP) layers are
stacked to progressively exploit the differencing features of bitemporal
features. To sufficiently utilize the extracted multi-scale features for
captioning, we propose a scale-aware reinforcement (SR) module and combine it
with the Transformer decoding layer to progressively utilize the features from
different PDP layers. Experiments show that the PDP layer and SR module are
effective and our PSNet outperforms previous methods.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 09:33:49 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Liu",
"Chenyang",
""
],
[
"Yang",
"Jiajun",
""
],
[
"Qi",
"Zipeng",
""
],
[
"Zou",
"Zhengxia",
""
],
[
"Shi",
"Zhenwei",
""
]
] |
new_dataset
| 0.952606 |
2303.00458
|
Manos Kamarianakis
|
Manos Kamarianakis, Antonis Protopsaltis, George Papagiannakis
|
AR-Assisted Surgical Care via 5G networks for First Aid Responders
|
3 pages, 2 figures, presented at IEEE International Workshop on
Computer Aided Modeling and Design of Communication Links and Networks
(CAMAD) 2022, 2-3 November 2022
| null | null | null |
cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Surgeons should play a central role in disaster planning and management due
to the overwhelming number of bodily injuries that are typically involved
during most forms of disaster. In fact, various types of surgical procedures
are performed by emergency medical teams after sudden-onset disasters, such as
soft tissue wounds, orthopaedic traumas, abdominal surgeries, etc. HMD-based
Augmented Reality (AR), using state-of-the-art hardware such as the Magic Leap
or the Microsoft HoloLens, have long been foreseen as a key enabler for
clinicians in surgical use cases, especially for procedures performed outside
of the operating room.
This paper describes the Use Case (UC) "AR-assisted emergency surgical care",
identified in the context of the 5G-EPICENTRE EU-funded project. Specifically,
the UC will experiment with holographic AR technology for emergency medical
surgery teams, by overlaying deformable medical models directly on top of the
patient body parts, effectively enabling surgeons to see inside (visualizing
bones, blood vessels, etc.) and perform surgical actions following step-by-step
instructions. The goal is to combine the computational and data-intensive
nature of AR and Computer Vision algorithms with upcoming 5G network
architectures deployed for edge computing so as to satisfy real-time
interaction requirements and provide an efficient and powerful platform for the
pervasive promotion of such applications. By developing the necessary Virtual
Network Functions (VNFs) to manage data-intensive services (e.g., prerendering,
caching, compression) and by exploiting available network resources and
Multi-access Edge Computing (MEC) support, provided by the 5G-EPICENTRE
infrastructure, this UC aims to provide powerful AR-based tools, usable on
site, to first-aid responders.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 12:33:31 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Kamarianakis",
"Manos",
""
],
[
"Protopsaltis",
"Antonis",
""
],
[
"Papagiannakis",
"George",
""
]
] |
new_dataset
| 0.993352 |
2303.00502
|
Zhe Niu
|
Zhe Niu and Brian Mak
|
On the Audio-visual Synchronization for Lip-to-Speech Synthesis
| null | null | null | null |
cs.SD cs.CV eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most lip-to-speech (LTS) synthesis models are trained and evaluated under the
assumption that the audio-video pairs in the dataset are perfectly
synchronized. In this work, we show that the commonly used audio-visual
datasets, such as GRID, TCD-TIMIT, and Lip2Wav, can have data asynchrony
issues. Training lip-to-speech with such datasets may further cause the model
asynchrony issue -- that is, the generated speech and the input video are out
of sync. To address these asynchrony issues, we propose a synchronized
lip-to-speech (SLTS) model with an automatic synchronization mechanism (ASM) to
correct data asynchrony and penalize model asynchrony. We further demonstrate
the limitation of the commonly adopted evaluation metrics for LTS with
asynchronous test data and introduce an audio alignment frontend before the
metrics sensitive to time alignment for better evaluation. We compare our
method with state-of-the-art approaches on conventional and time-aligned
metrics to show the benefits of synchronization training.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 13:35:35 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Niu",
"Zhe",
""
],
[
"Mak",
"Brian",
""
]
] |
new_dataset
| 0.99704 |
2303.00532
|
Christian Lienen
|
Christian Lienen, Sorel Horst Middeke, and Marco Platzner
|
fpgaDDS: An Intra-FPGA Data Distribution Service for ROS 2 Robotics
Applications
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Modern computing platforms for robotics applications comprise a set of
heterogeneous elements, e.g., multi-core CPUs, embedded GPUs, and FPGAs. FPGAs
are reprogrammable hardware devices that allow for fast and energy-efficient
computation of many relevant tasks in robotics. ROS is the de-facto programming
standard for robotics and decomposes an application into a set of communicating
nodes. ReconROS is a previous approach that can map complete ROS nodes into
hardware for acceleration. Since ReconROS relies on standard ROS communication
layers, exchanging data between hardware-mapped nodes can lead to a performance
bottleneck.
This paper presents fpgaDDS, a lean data distribution service for
hardware-mapped ROS 2 nodes. fpgaDDS relies on a customized and statically
generated streaming-based communication architecture. We detail this
communication architecture with its components and outline its benefits. We
evaluate fpgaDDS on a test example and a larger autonomous vehicle case study.
Compared to a ROS 2 application in software, we achieve speedups of up to 13.34
and reduce jitter by two orders of magnitude.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 14:13:52 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Lienen",
"Christian",
""
],
[
"Middeke",
"Sorel Horst",
""
],
[
"Platzner",
"Marco",
""
]
] |
new_dataset
| 0.999554 |
2303.00534
|
Zheng Yuan
|
Zheng Yuan, Qiao Jin, Chuanqi Tan, Zhengyun Zhao, Hongyi Yuan, Fei
Huang, Songfang Huang
|
RAMM: Retrieval-augmented Biomedical Visual Question Answering with
Multi-modal Pre-training
| null | null | null | null |
cs.CV cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Vision-and-language multi-modal pretraining and fine-tuning have shown great
success in visual question answering (VQA). Compared to general domain VQA, the
performance of biomedical VQA suffers from limited data. In this paper, we
propose a retrieval-augmented pretrain-and-finetune paradigm named RAMM for
biomedical VQA to overcome the data limitation issue. Specifically, we collect
a new biomedical dataset named PMCPM which offers patient-based image-text
pairs containing diverse patient situations from PubMed. Then, we pretrain the
biomedical multi-modal model to learn visual and textual representation for
image-text pairs and align these representations with image-text contrastive
objective (ITC). Finally, we propose a retrieval-augmented method to better use
the limited data. We propose to retrieve similar image-text pairs based on ITC
from pretraining datasets and introduce a novel retrieval-attention module to
fuse the representation of the image and the question with the retrieved images
and texts. Experiments demonstrate that our retrieval-augmented
pretrain-and-finetune paradigm obtains state-of-the-art performance on
Med-VQA2019, Med-VQA2021, VQARAD, and SLAKE datasets. Further analysis shows
that the proposed RAMM and PMCPM can enhance biomedical VQA performance
compared with previous resources and methods. We will open-source our dataset,
codes, and pretrained model.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 14:21:19 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Yuan",
"Zheng",
""
],
[
"Jin",
"Qiao",
""
],
[
"Tan",
"Chuanqi",
""
],
[
"Zhao",
"Zhengyun",
""
],
[
"Yuan",
"Hongyi",
""
],
[
"Huang",
"Fei",
""
],
[
"Huang",
"Songfang",
""
]
] |
new_dataset
| 0.994572 |
2303.00703
|
Renrui Zhang
|
Renrui Zhang, Liuhui Wang, Ziyu Guo, Jianbo Shi
|
Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis
|
Accepted by WACV 2023
|
Proceedings of the IEEE/CVF Winter Conference on Applications of
Computer Vision (WACV), 2023, pp. 1246-1255
| null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Performances on standard 3D point cloud benchmarks have plateaued, resulting
in oversized models and complex network design to make a fractional
improvement. We present an alternative to enhance existing deep neural networks
without any redesigning or extra parameters, termed as Spatial-Neighbor Adapter
(SN-Adapter). Building on any trained 3D network, we utilize its learned
encoding capability to extract features of the training dataset and summarize
them as prototypical spatial knowledge. For a test point cloud, the SN-Adapter
retrieves k nearest neighbors (k-NN) from the pre-constructed spatial
prototypes and linearly interpolates the k-NN prediction with that of the
original 3D network. By providing complementary characteristics, the proposed
SN-Adapter serves as a plug-and-play module to economically improve performance
in a non-parametric manner. More importantly, our SN-Adapter can be effectively
generalized to various 3D tasks, including shape classification, part
segmentation, and 3D object detection, demonstrating its superiority and
robustness. We hope our approach could show a new perspective for point cloud
analysis and facilitate future research.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 17:57:09 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Zhang",
"Renrui",
""
],
[
"Wang",
"Liuhui",
""
],
[
"Guo",
"Ziyu",
""
],
[
"Shi",
"Jianbo",
""
]
] |
new_dataset
| 0.992156 |
2303.00725
|
Saghir Alfasly
|
Saghir Alfasly, Zaid Al-huda, Saifullah Bello, Ahmed Elazab, Jian Lu,
Chen Xu
|
OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Current deep models provide remarkable object detection in terms of object
classification and localization. However, estimating object rotation with
respect to other visual objects in the visual context of an input image still
lacks deep studies due to the unavailability of object datasets with rotation
annotations.
This paper tackles these two challenges to solve the rotation estimation of a
parked bike with respect to its parking area. First, we leverage the power of
3D graphics to build a camera-agnostic well-annotated Synthetic Bike Rotation
Dataset (SynthBRSet). Then, we propose an object-to-spot rotation estimator
(OSRE) by extending the object detection task to further regress the bike
rotations in two axes. Since our model is purely trained on synthetic data, we
adopt image smoothing techniques when deploying it on real-world images. The
proposed OSRE is evaluated on synthetic and real-world data providing promising
results. Our data and code are available at
\href{https://github.com/saghiralfasly/OSRE-Project}{https://github.com/saghiralfasly/OSRE-Project}.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 18:34:10 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Alfasly",
"Saghir",
""
],
[
"Al-huda",
"Zaid",
""
],
[
"Bello",
"Saifullah",
""
],
[
"Elazab",
"Ahmed",
""
],
[
"Lu",
"Jian",
""
],
[
"Xu",
"Chen",
""
]
] |
new_dataset
| 0.999632 |
2303.00749
|
ZiYang Xie
|
Ziyang Xie, Junge Zhang, Wenye Li, Feihu Zhang, Li Zhang
|
S-NeRF: Neural Radiance Fields for Street Views
|
ICLR 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and
scenes, given the object-centric camera views with large overlaps. However, we
conjugate that this paradigm does not fit the nature of the street views that
are collected by many self-driving cars from the large-scale unbounded scenes.
Also, the onboard cameras perceive scenes without much overlapping. Thus,
existing NeRFs often produce blurs, 'floaters' and other artifacts on
street-view synthesis. In this paper, we propose a new street-view NeRF
(S-NeRF) that considers novel view synthesis of both the large-scale background
scenes and the foreground moving vehicles jointly. Specifically, we improve the
scene parameterization function and the camera poses for learning better neural
representations from street views. We also use the the noisy and sparse LiDAR
points to boost the training and learn a robust geometry and reprojection based
confidence to address the depth outliers. Moreover, we extend our S-NeRF for
reconstructing moving vehicles that is impracticable for conventional NeRFs.
Thorough experiments on the large-scale driving datasets (e.g., nuScenes and
Waymo) demonstrate that our method beats the state-of-the-art rivals by
reducing 7% to 40% of the mean-squared error in the street-view synthesis and a
45% PSNR gain for the moving vehicles rendering.
|
[
{
"version": "v1",
"created": "Wed, 1 Mar 2023 18:59:30 GMT"
}
] | 2023-03-02T00:00:00 |
[
[
"Xie",
"Ziyang",
""
],
[
"Zhang",
"Junge",
""
],
[
"Li",
"Wenye",
""
],
[
"Zhang",
"Feihu",
""
],
[
"Zhang",
"Li",
""
]
] |
new_dataset
| 0.99257 |
2006.02854
|
Christian Anti\'c
|
Christian Anti\'c
|
Analogical proportions
| null | null | null | null |
cs.LO cs.AI cs.LG cs.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Analogy-making is at the core of human and artificial intelligence and
creativity with applications to such diverse tasks as proving mathematical
theorems and building mathematical theories, common sense reasoning, learning,
language acquisition, and story telling. This paper introduces from first
principles an abstract algebraic framework of analogical proportions of the
form `$a$ is to $b$ what $c$ is to $d$' in the general setting of universal
algebra. This enables us to compare mathematical objects possibly across
different domains in a uniform way which is crucial for AI-systems. It turns
out that our notion of analogical proportions has appealing mathematical
properties. As we construct our model from first principles using only
elementary concepts of universal algebra, and since our model questions some
basic properties of analogical proportions presupposed in the literature, to
convince the reader of the plausibility of our model we show that it can be
naturally embedded into first-order logic via model-theoretic types and prove
from that perspective that analogical proportions are compatible with
structure-preserving mappings. This provides conceptual evidence for its
applicability. In a broader sense, this paper is a first step towards a theory
of analogical reasoning and learning systems with potential applications to
fundamental AI-problems like common sense reasoning and computational learning
and creativity.
|
[
{
"version": "v1",
"created": "Thu, 4 Jun 2020 13:44:36 GMT"
},
{
"version": "v10",
"created": "Sat, 4 Dec 2021 16:24:42 GMT"
},
{
"version": "v11",
"created": "Fri, 18 Feb 2022 17:16:25 GMT"
},
{
"version": "v12",
"created": "Mon, 14 Mar 2022 17:29:07 GMT"
},
{
"version": "v13",
"created": "Sun, 8 May 2022 12:15:52 GMT"
},
{
"version": "v14",
"created": "Tue, 28 Feb 2023 16:47:25 GMT"
},
{
"version": "v2",
"created": "Sun, 7 Jun 2020 13:54:42 GMT"
},
{
"version": "v3",
"created": "Tue, 25 Aug 2020 14:30:38 GMT"
},
{
"version": "v4",
"created": "Thu, 10 Dec 2020 14:52:31 GMT"
},
{
"version": "v5",
"created": "Sat, 17 Apr 2021 14:36:37 GMT"
},
{
"version": "v6",
"created": "Tue, 25 May 2021 12:12:56 GMT"
},
{
"version": "v7",
"created": "Sun, 15 Aug 2021 14:21:56 GMT"
},
{
"version": "v8",
"created": "Mon, 22 Nov 2021 20:59:26 GMT"
},
{
"version": "v9",
"created": "Wed, 24 Nov 2021 21:50:43 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Antić",
"Christian",
""
]
] |
new_dataset
| 0.950269 |
2105.01306
|
Youngseo Son
|
Youngseo Son, Vasudha Varadarajan, H Andrew Schwartz
|
Discourse Relation Embeddings: Representing the Relations between
Discourse Segments in Social Media
|
Published in EMNLP 2022 UM-IoS
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Discourse relations are typically modeled as a discrete class that
characterizes the relation between segments of text (e.g. causal explanations,
expansions). However, such predefined discrete classes limits the universe of
potential relationships and their nuanced differences. Analogous to contextual
word embeddings, we propose representing discourse relations as points in high
dimensional continuous space. However, unlike words, discourse relations often
have no surface form (relations are between two segments, often with no word or
phrase in that gap) which presents a challenge for existing embedding
techniques. We present a novel method for automatically creating discourse
relation embeddings (DiscRE), addressing the embedding challenge through a
weakly supervised, multitask approach to learn diverse and nuanced relations
between discourse segments in social media. Results show DiscRE can: (1) obtain
the best performance on Twitter discourse relation classification task (macro
F1=0.76) (2) improve the state of the art in social media causality prediction
(from F1=.79 to .81), (3) perform beyond modern sentence and contextual word
embeddings at traditional discourse relation classification, and (4) capture
novel nuanced relations (e.g. relations semantically at the intersection of
causal explanations and counterfactuals).
|
[
{
"version": "v1",
"created": "Tue, 4 May 2021 05:58:27 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 06:17:38 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Son",
"Youngseo",
""
],
[
"Varadarajan",
"Vasudha",
""
],
[
"Schwartz",
"H Andrew",
""
]
] |
new_dataset
| 0.989213 |
2106.13201
|
Chengxi Li
|
Chengxi Li, Stanley H. Chan, Yi-Ting Chen
|
DROID: Driver-centric Risk Object Identification
|
Submitted to TPAMI
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Identification of high-risk driving situations is generally approached
through collision risk estimation or accident pattern recognition. In this
work, we approach the problem from the perspective of subjective risk. We
operationalize subjective risk assessment by predicting driver behavior changes
and identifying the cause of changes. To this end, we introduce a new task
called driver-centric risk object identification (DROID), which uses egocentric
video to identify object(s) influencing a driver's behavior, given only the
driver's response as the supervision signal. We formulate the task as a
cause-effect problem and present a novel two-stage DROID framework, taking
inspiration from models of situation awareness and causal inference. A subset
of data constructed from the Honda Research Institute Driving Dataset (HDD) is
used to evaluate DROID. We demonstrate state-of-the-art DROID performance, even
compared with strong baseline models using this dataset. Additionally, we
conduct extensive ablative studies to justify our design choices. Moreover, we
demonstrate the applicability of DROID for risk assessment.
|
[
{
"version": "v1",
"created": "Thu, 24 Jun 2021 17:27:32 GMT"
},
{
"version": "v2",
"created": "Fri, 7 Oct 2022 05:22:19 GMT"
},
{
"version": "v3",
"created": "Tue, 28 Feb 2023 17:36:38 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Li",
"Chengxi",
""
],
[
"Chan",
"Stanley H.",
""
],
[
"Chen",
"Yi-Ting",
""
]
] |
new_dataset
| 0.999502 |
2110.06651
|
Linhan Zhang
|
Linhan Zhang, Qian Chen, Wen Wang, Chong Deng, Shiliang Zhang, Bing
Li, Wei Wang, Xin Cao
|
MDERank: A Masked Document Embedding Rank Approach for Unsupervised
Keyphrase Extraction
|
13 pages, 5 figures
|
Finding of The 60st Annual Meeting of the Association for
Computational Linguistics, 2022
| null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Keyphrase extraction (KPE) automatically extracts phrases in a document that
provide a concise summary of the core content, which benefits downstream
information retrieval and NLP tasks. Previous state-of-the-art (SOTA) methods
select candidate keyphrases based on the similarity between learned
representations of the candidates and the document. They suffer performance
degradation on long documents due to discrepancy between sequence lengths which
causes mismatch between representations of keyphrase candidates and the
document. In this work, we propose a novel unsupervised embedding-based KPE
approach, Masked Document Embedding Rank (MDERank), to address this problem by
leveraging a mask strategy and ranking candidates by the similarity between
embeddings of the source document and the masked document. We further develop a
KPE-oriented BERT (KPEBERT) model by proposing a novel self-supervised
contrastive learning method, which is more compatible to MDERank than vanilla
BERT. Comprehensive evaluations on six KPE benchmarks demonstrate that the
proposed MDERank outperforms state-of-the-art unsupervised KPE approach by
average 1.80 $F1@15$ improvement. MDERank further benefits from KPEBERT and
overall achieves average 3.53 $F1@15$ improvement over the SOTA SIFRank. Our
code is available at \url{https://github.com/LinhanZ/mderank}.
|
[
{
"version": "v1",
"created": "Wed, 13 Oct 2021 11:29:17 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Mar 2022 09:07:29 GMT"
},
{
"version": "v3",
"created": "Tue, 28 Feb 2023 00:54:45 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Zhang",
"Linhan",
""
],
[
"Chen",
"Qian",
""
],
[
"Wang",
"Wen",
""
],
[
"Deng",
"Chong",
""
],
[
"Zhang",
"Shiliang",
""
],
[
"Li",
"Bing",
""
],
[
"Wang",
"Wei",
""
],
[
"Cao",
"Xin",
""
]
] |
new_dataset
| 0.973285 |
2112.02500
|
Peng Zheng
|
Jie Qin, Peng Zheng, Yichao Yan, Rong Quan, Xiaogang Cheng, Bingbing
Ni
|
MovieNet-PS: A Large-Scale Person Search Dataset in the Wild
|
ICASSP 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Person search aims to jointly localize and identify a query person from
natural, uncropped images, which has been actively studied over the past few
years. In this paper, we delve into the rich context information globally and
locally surrounding the target person, which we refer to as scene and group
context, respectively. Unlike previous works that treat the two types of
context individually, we exploit them in a unified global-local context network
(GLCNet) with the intuitive aim of feature enhancement. Specifically, re-ID
embeddings and context features are simultaneously learned in a multi-stage
fashion, ultimately leading to enhanced, discriminative features for person
search. We conduct the experiments on two person search benchmarks (i.e.,
CUHK-SYSU and PRW) as well as extend our approach to a more challenging setting
(i.e., character search on MovieNet). Extensive experimental results
demonstrate the consistent improvement of the proposed GLCNet over the
state-of-the-art methods on all three datasets. Our source codes, pre-trained
models, and the new dataset are publicly available at:
https://github.com/ZhengPeng7/GLCNet.
|
[
{
"version": "v1",
"created": "Sun, 5 Dec 2021 07:38:53 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Mar 2022 11:11:26 GMT"
},
{
"version": "v3",
"created": "Tue, 12 Apr 2022 13:20:39 GMT"
},
{
"version": "v4",
"created": "Tue, 28 Feb 2023 11:19:31 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Qin",
"Jie",
""
],
[
"Zheng",
"Peng",
""
],
[
"Yan",
"Yichao",
""
],
[
"Quan",
"Rong",
""
],
[
"Cheng",
"Xiaogang",
""
],
[
"Ni",
"Bingbing",
""
]
] |
new_dataset
| 0.999767 |
2201.06435
|
Selahattin Cansiz
|
Selahattin Cansiz, Cem Kesim, Sevval Nur Bektas, Zeynep Kulali, Murat
Hasanreisoglu, Cigdem Gunduz-Demir
|
FourierNet: Shape-Preserving Network for Henle's Fiber Layer
Segmentation in Optical Coherence Tomography Images
| null |
IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 2,
pp. 1036-1047, Feb. 2023
|
10.1109/JBHI.2022.3225425
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Henle's fiber layer (HFL) in the retina carries valuable information on
the macular condition of an eye. However, in the common practice, this layer is
not separately segmented but rather included in the outer nuclear layer since
it is difficult to perceive HFL contours on standard optical coherence
tomography (OCT) imaging. Due to its variable reflectivity under an imaging
beam, delineating the HFL contours necessitates directional OCT, which requires
additional imaging. This paper addresses this issue by introducing a
shape-preserving network, FourierNet, that achieves HFL segmentation in
standard OCT scans with the target performance obtained when directional OCT
scans are used. FourierNet is a new cascaded network design that puts forward
the idea of benefiting the shape prior of HFL in the network training. This
design proposes to represent the shape prior by extracting Fourier descriptors
on the HFL contours and defining an additional regression task of learning
these descriptors. It then formulates HFL segmentation as concurrent learning
of regression and classification tasks, in which Fourier descriptors are
estimated from an input image to encode the shape prior and used together with
the input image to construct the HFL segmentation map. Our experiments on 1470
images of 30 OCT scans reveal that quantifying the HFL shape with Fourier
descriptors and concurrently learning them with the main task of HFL
segmentation lead to better results. This indicates the effectiveness of
designing a shape-preserving network to improve HFL segmentation by reducing
the need to perform directional OCT imaging.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 14:50:26 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Cansiz",
"Selahattin",
""
],
[
"Kesim",
"Cem",
""
],
[
"Bektas",
"Sevval Nur",
""
],
[
"Kulali",
"Zeynep",
""
],
[
"Hasanreisoglu",
"Murat",
""
],
[
"Gunduz-Demir",
"Cigdem",
""
]
] |
new_dataset
| 0.998959 |
2203.13474
|
Erik Nijkamp Dr.
|
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo
Zhou, Silvio Savarese, Caiming Xiong
|
CodeGen: An Open Large Language Model for Code with Multi-Turn Program
Synthesis
| null | null | null | null |
cs.LG cs.CL cs.PL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Program synthesis strives to generate a computer program as a solution to a
given problem specification, expressed with input-output examples or natural
language descriptions. The prevalence of large language models advances the
state-of-the-art for program synthesis, though limited training resources and
data impede open access to such models. To democratize this, we train and
release a family of large language models up to 16.1B parameters, called
CODEGEN, on natural language and programming language data, and open source the
training library JAXFORMER. We show the utility of the trained model by
demonstrating that it is competitive with the previous state-of-the-art on
zero-shot Python code generation on HumanEval. We further investigate the
multi-step paradigm for program synthesis, where a single program is factorized
into multiple prompts specifying subproblems. To this end, we construct an open
benchmark, Multi-Turn Programming Benchmark (MTPB), consisting of 115 diverse
problem sets that are factorized into multi-turn prompts. Our analysis on MTPB
shows that the same intent provided to CODEGEN in multi-turn fashion
significantly improves program synthesis over that provided as a single turn.
We make the training library JAXFORMER and model checkpoints available as open
source contribution: https://github.com/salesforce/CodeGen.
|
[
{
"version": "v1",
"created": "Fri, 25 Mar 2022 06:55:15 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Mar 2022 17:10:30 GMT"
},
{
"version": "v3",
"created": "Wed, 30 Mar 2022 06:57:04 GMT"
},
{
"version": "v4",
"created": "Thu, 29 Sep 2022 20:43:54 GMT"
},
{
"version": "v5",
"created": "Mon, 27 Feb 2023 21:26:48 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Nijkamp",
"Erik",
""
],
[
"Pang",
"Bo",
""
],
[
"Hayashi",
"Hiroaki",
""
],
[
"Tu",
"Lifu",
""
],
[
"Wang",
"Huan",
""
],
[
"Zhou",
"Yingbo",
""
],
[
"Savarese",
"Silvio",
""
],
[
"Xiong",
"Caiming",
""
]
] |
new_dataset
| 0.996476 |
2206.07754
|
Katherine O'Toole
|
Katherine O'Toole and Em\H{o}ke-\'Agnes Horv\'at
|
Novelty and Cultural Evolution in Modern Popular Music
| null |
EPJ Data Science 12 (2023) 1-25
|
10.1140/epjds/s13688-023-00377-7
| null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
The ubiquity of digital music consumption has made it possible to extract
information about modern music that allows us to perform large scale analysis
of stylistic change over time. In order to uncover underlying patterns in
cultural evolution, we examine the relationship between the established
characteristics of different genres and styles, and the introduction of novel
ideas that fuel this ongoing creative evolution. To understand how this dynamic
plays out and shapes the cultural ecosystem, we compare musical artifacts to
their contemporaries to identify novel artifacts, study the relationship
between novelty and commercial success, and connect this to the changes in
musical content that we can observe over time. Using Music Information
Retrieval (MIR) data and lyrics from Billboard Hot 100 songs between 1974-2013,
we calculate a novelty score for each song's aural attributes and lyrics.
Comparing both scores to the popularity of the song following its release, we
uncover key patterns in the relationship between novelty and audience
reception. Additionally, we look at the link between novelty and the likelihood
that a song was influential given where its MIR and lyrical features fit within
the larger trends we observed.
|
[
{
"version": "v1",
"created": "Wed, 15 Jun 2022 18:25:39 GMT"
},
{
"version": "v2",
"created": "Wed, 26 Oct 2022 19:05:54 GMT"
},
{
"version": "v3",
"created": "Mon, 27 Feb 2023 21:34:59 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"O'Toole",
"Katherine",
""
],
[
"Horvát",
"Emőke-Ágnes",
""
]
] |
new_dataset
| 0.996752 |
2209.06628
|
Fangcheng Zhu
|
Fangcheng Zhu, Yunfan Ren, Fanze Kong, Huajie Wu, Siqi Liang, Nan
Chen, Wei Xu, Fu Zhang
|
Swarm-LIO: Decentralized Swarm LiDAR-inertial Odometry
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate self and relative state estimation are the critical preconditions
for completing swarm tasks, e.g., collaborative autonomous exploration, target
tracking, search and rescue. This paper proposes Swarm-LIO: a fully
decentralized state estimation method for aerial swarm systems, in which each
drone performs precise ego-state estimation, exchanges ego-state and mutual
observation information by wireless communication, and estimates relative state
with respect to (w.r.t.) the rest of UAVs, all in real-time and only based on
LiDAR-inertial measurements. A novel 3D LiDAR-based drone detection,
identification and tracking method is proposed to obtain observations of
teammate drones. The mutual observation measurements are then tightly-coupled
with IMU and LiDAR measurements to perform real-time and accurate estimation of
ego-state and relative state jointly. Extensive real-world experiments show the
broad adaptability to complicated scenarios, including GPS-denied scenes,
degenerate scenes for camera (dark night) or LiDAR (facing a single wall).
Compared with ground-truth provided by motion capture system, the result shows
the centimeter-level localization accuracy which outperforms other
state-of-the-art LiDAR-inertial odometry for single UAV system.
|
[
{
"version": "v1",
"created": "Wed, 14 Sep 2022 13:24:34 GMT"
},
{
"version": "v2",
"created": "Sat, 25 Feb 2023 15:00:05 GMT"
},
{
"version": "v3",
"created": "Tue, 28 Feb 2023 10:47:36 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Zhu",
"Fangcheng",
""
],
[
"Ren",
"Yunfan",
""
],
[
"Kong",
"Fanze",
""
],
[
"Wu",
"Huajie",
""
],
[
"Liang",
"Siqi",
""
],
[
"Chen",
"Nan",
""
],
[
"Xu",
"Wei",
""
],
[
"Zhang",
"Fu",
""
]
] |
new_dataset
| 0.970019 |
2209.07734
|
Zhenhua Xu
|
Zhenhua Xu, Yuxuan Liu, Yuxiang Sun, Ming Liu, Lujia Wang
|
CenterLineDet: CenterLine Graph Detection for Road Lanes with
Vehicle-mounted Sensors by Transformer for HD Map Generation
|
ICRA 2023
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the fast development of autonomous driving technologies, there is an
increasing demand for high-definition (HD) maps, which provide reliable and
robust prior information about the static part of the traffic environments. As
one of the important elements in HD maps, road lane centerline is critical for
downstream tasks, such as prediction and planning. Manually annotating
centerlines for road lanes in HD maps is labor-intensive, expensive and
inefficient, severely restricting the wide applications of autonomous driving
systems. Previous work seldom explores the lane centerline detection problem
due to the complicated topology and severe overlapping issues of lane
centerlines. In this paper, we propose a novel method named CenterLineDet to
detect lane centerlines for automatic HD map generation. Our CenterLineDet is
trained by imitation learning and can effectively detect the graph of
centerlines with vehicle-mounted sensors (i.e., six cameras and one LiDAR)
through iterations. Due to the use of the DETR-like transformer network,
CenterLineDet can handle complicated graph topology, such as lane
intersections. The proposed approach is evaluated on the large-scale public
dataset NuScenes. The superiority of our CenterLineDet is demonstrated by the
comparative results. Our code, supplementary materials, and video
demonstrations are available at
\href{https://tonyxuqaq.github.io/projects/CenterLineDet/}{https://tonyxuqaq.github.io/projects/CenterLineDet/}.
|
[
{
"version": "v1",
"created": "Fri, 16 Sep 2022 06:15:26 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 10:44:34 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Xu",
"Zhenhua",
""
],
[
"Liu",
"Yuxuan",
""
],
[
"Sun",
"Yuxiang",
""
],
[
"Liu",
"Ming",
""
],
[
"Wang",
"Lujia",
""
]
] |
new_dataset
| 0.999826 |
2209.08772
|
Jingxi Xu
|
Jingxi Xu, Han Lin, Shuran Song, Matei Ciocarlie
|
TANDEM3D: Active Tactile Exploration for 3D Object Recognition
|
7 pages. Accepted to International Conference on Robotics and
Automation (ICRA) 2023
| null | null | null |
cs.CV cs.AI cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Tactile recognition of 3D objects remains a challenging task. Compared to 2D
shapes, the complex geometry of 3D surfaces requires richer tactile signals,
more dexterous actions, and more advanced encoding techniques. In this work, we
propose TANDEM3D, a method that applies a co-training framework for exploration
and decision making to 3D object recognition with tactile signals. Starting
with our previous work, which introduced a co-training paradigm for 2D
recognition problems, we introduce a number of advances that enable us to scale
up to 3D. TANDEM3D is based on a novel encoder that builds 3D object
representation from contact positions and normals using PointNet++.
Furthermore, by enabling 6DOF movement, TANDEM3D explores and collects
discriminative touch information with high efficiency. Our method is trained
entirely in simulation and validated with real-world experiments. Compared to
state-of-the-art baselines, TANDEM3D achieves higher accuracy and a lower
number of actions in recognizing 3D objects and is also shown to be more robust
to different types and amounts of sensor noise. Video is available at
https://jxu.ai/tandem3d.
|
[
{
"version": "v1",
"created": "Mon, 19 Sep 2022 05:54:26 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 05:22:09 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Xu",
"Jingxi",
""
],
[
"Lin",
"Han",
""
],
[
"Song",
"Shuran",
""
],
[
"Ciocarlie",
"Matei",
""
]
] |
new_dataset
| 0.990744 |
2209.09489
|
Zicheng Zhang
|
Zicheng Zhang, Yingjie Zhou, Wei Sun, Xiongkuo Min, Yuzhe Wu, Guangtao
Zhai
|
Perceptual Quality Assessment for Digital Human Heads
| null | null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Digital humans are attracting more and more research interest during the last
decade, the generation, representation, rendering, and animation of which have
been put into large amounts of effort. However, the quality assessment of
digital humans has fallen behind. Therefore, to tackle the challenge of digital
human quality assessment issues, we propose the first large-scale quality
assessment database for three-dimensional (3D) scanned digital human heads
(DHHs). The constructed database consists of 55 reference DHHs and 1,540
distorted DHHs along with the subjective perceptual ratings. Then, a simple yet
effective full-reference (FR) projection-based method is proposed to evaluate
the visual quality of DHHs. The pretrained Swin Transformer tiny is employed
for hierarchical feature extraction and the multi-head attention module is
utilized for feature fusion. The experimental results reveal that the proposed
method exhibits state-of-the-art performance among the mainstream FR metrics.
The database is released at https://github.com/zzc-1998/DHHQA.
|
[
{
"version": "v1",
"created": "Tue, 20 Sep 2022 06:02:57 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2022 08:15:37 GMT"
},
{
"version": "v3",
"created": "Tue, 10 Jan 2023 07:00:01 GMT"
},
{
"version": "v4",
"created": "Sun, 26 Feb 2023 08:22:09 GMT"
},
{
"version": "v5",
"created": "Tue, 28 Feb 2023 12:15:46 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Zhang",
"Zicheng",
""
],
[
"Zhou",
"Yingjie",
""
],
[
"Sun",
"Wei",
""
],
[
"Min",
"Xiongkuo",
""
],
[
"Wu",
"Yuzhe",
""
],
[
"Zhai",
"Guangtao",
""
]
] |
new_dataset
| 0.993586 |
2210.09957
|
Virginie Do
|
Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric and
Nicolas Usunier
|
Contextual bandits with concave rewards, and an application to fair
ranking
|
ICLR 2023
| null | null | null |
cs.LG cs.AI cs.CY cs.IR stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
We consider Contextual Bandits with Concave Rewards (CBCR), a multi-objective
bandit problem where the desired trade-off between the rewards is defined by a
known concave objective function, and the reward vector depends on an observed
stochastic context. We present the first algorithm with provably vanishing
regret for CBCR without restrictions on the policy space, whereas prior works
were restricted to finite policy spaces or tabular representations. Our
solution is based on a geometric interpretation of CBCR algorithms as
optimization algorithms over the convex set of expected rewards spanned by all
stochastic policies. Building on Frank-Wolfe analyses in constrained convex
optimization, we derive a novel reduction from the CBCR regret to the regret of
a scalar-reward bandit problem. We illustrate how to apply the reduction
off-the-shelf to obtain algorithms for CBCR with both linear and general reward
functions, in the case of non-combinatorial actions. Motivated by fairness in
recommendation, we describe a special case of CBCR with rankings and
fairness-aware objectives, leading to the first algorithm with regret
guarantees for contextual combinatorial bandits with fairness of exposure.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 16:11:55 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 10:26:48 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Do",
"Virginie",
""
],
[
"Dohmatob",
"Elvis",
""
],
[
"Pirotta",
"Matteo",
""
],
[
"Lazaric",
"Alessandro",
""
],
[
"Usunier",
"Nicolas",
""
]
] |
new_dataset
| 0.99876 |
2212.08377
|
Yufeng Zheng
|
Yufeng Zheng, Wang Yifan, Gordon Wetzstein, Michael J. Black, Otmar
Hilliges
|
PointAvatar: Deformable Point-based Head Avatars from Videos
|
Project page: https://zhengyuf.github.io/PointAvatar/ Code base:
https://github.com/zhengyuf/pointavatar
| null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ability to create realistic, animatable and relightable head avatars from
casual video sequences would open up wide ranging applications in communication
and entertainment. Current methods either build on explicit 3D morphable meshes
(3DMM) or exploit neural implicit representations. The former are limited by
fixed topology, while the latter are non-trivial to deform and inefficient to
render. Furthermore, existing approaches entangle lighting in the color
estimation, thus they are limited in re-rendering the avatar in new
environments. In contrast, we propose PointAvatar, a deformable point-based
representation that disentangles the source color into intrinsic albedo and
normal-dependent shading. We demonstrate that PointAvatar bridges the gap
between existing mesh- and implicit representations, combining high-quality
geometry and appearance with topological flexibility, ease of deformation and
rendering efficiency. We show that our method is able to generate animatable 3D
avatars using monocular videos from multiple sources including hand-held
smartphones, laptop webcams and internet videos, achieving state-of-the-art
quality in challenging cases where previous methods fail, e.g., thin hair
strands, while being significantly more efficient in training than competing
methods.
|
[
{
"version": "v1",
"created": "Fri, 16 Dec 2022 10:05:31 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 09:00:33 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Zheng",
"Yufeng",
""
],
[
"Yifan",
"Wang",
""
],
[
"Wetzstein",
"Gordon",
""
],
[
"Black",
"Michael J.",
""
],
[
"Hilliges",
"Otmar",
""
]
] |
new_dataset
| 0.99781 |
2301.06958
|
Shusheng Yang
|
Shusheng Yang, Yixiao Ge, Kun Yi, Dian Li, Ying Shan, Xiaohu Qie,
Xinggang Wang
|
RILS: Masked Visual Reconstruction in Language Semantic Space
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Both masked image modeling (MIM) and natural language supervision have
facilitated the progress of transferable visual pre-training. In this work, we
seek the synergy between two paradigms and study the emerging properties when
MIM meets natural language supervision. To this end, we present a novel masked
visual Reconstruction In Language semantic Space (RILS) pre-training framework,
in which sentence representations, encoded by the text encoder, serve as
prototypes to transform the vision-only signals into patch-sentence
probabilities as semantically meaningful MIM reconstruction targets. The vision
models can therefore capture useful components with structured information by
predicting proper semantic of masked tokens. Better visual representations
could, in turn, improve the text encoder via the image-text alignment
objective, which is essential for the effective MIM target transformation.
Extensive experimental results demonstrate that our method not only enjoys the
best of previous MIM and CLIP but also achieves further improvements on various
tasks due to their mutual benefits. RILS exhibits advanced transferability on
downstream classification, detection, and segmentation, especially for low-shot
regimes. Code will be made available at https://github.com/hustvl/RILS.
|
[
{
"version": "v1",
"created": "Tue, 17 Jan 2023 15:32:59 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 15:59:30 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Yang",
"Shusheng",
""
],
[
"Ge",
"Yixiao",
""
],
[
"Yi",
"Kun",
""
],
[
"Li",
"Dian",
""
],
[
"Shan",
"Ying",
""
],
[
"Qie",
"Xiaohu",
""
],
[
"Wang",
"Xinggang",
""
]
] |
new_dataset
| 0.950464 |
2302.04023
|
Yejin Bang
|
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su,
Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do,
Yan Xu, Pascale Fung
|
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on
Reasoning, Hallucination, and Interactivity
|
52 pages
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This paper proposes a framework for quantitatively evaluating interactive
LLMs such as ChatGPT using publicly available data sets. We carry out an
extensive technical evaluation of ChatGPT using 23 data sets covering 8
different common NLP application tasks. We evaluate the multitask, multilingual
and multi-modal aspects of ChatGPT based on these data sets and a newly
designed multimodal dataset. We find that ChatGPT outperforms LLMs with
zero-shot learning on most tasks and even outperforms fine-tuned models on some
tasks. We find that it is better at understanding non-Latin script languages
than generating them. It is able to generate multimodal content from textual
prompts, via an intermediate code generation step. Moreover, we find that
ChatGPT is 63.41% accurate on average in 10 different reasoning categories
under logical reasoning, non-textual reasoning, and commonsense reasoning,
hence making it an unreliable reasoner. It is, for example, better at deductive
than inductive reasoning. ChatGPT suffers from hallucination problems like
other LLMs and it generates more extrinsic hallucinations from its parametric
memory as it does not have access to an external knowledge base. Finally, the
interactive feature of ChatGPT enables human collaboration with the underlying
LLM to improve its performance, i.e, 8% ROUGE-1 on summarization and 2% ChrF++
on machine translation, in a multi-turn "prompt engineering" fashion. We also
release codebase for evaluation set extraction.
|
[
{
"version": "v1",
"created": "Wed, 8 Feb 2023 12:35:34 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 15:20:21 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Bang",
"Yejin",
""
],
[
"Cahyawijaya",
"Samuel",
""
],
[
"Lee",
"Nayeon",
""
],
[
"Dai",
"Wenliang",
""
],
[
"Su",
"Dan",
""
],
[
"Wilie",
"Bryan",
""
],
[
"Lovenia",
"Holy",
""
],
[
"Ji",
"Ziwei",
""
],
[
"Yu",
"Tiezheng",
""
],
[
"Chung",
"Willy",
""
],
[
"Do",
"Quyet V.",
""
],
[
"Xu",
"Yan",
""
],
[
"Fung",
"Pascale",
""
]
] |
new_dataset
| 0.98106 |
2302.12301
|
Rahul Deshmukh
|
Rahul Deshmukh, Constantine J. Roros, Amith Kashyap, Avinash C. Kak
|
An Aligned Multi-Temporal Multi-Resolution Satellite Image Dataset for
Change Detection Research
|
8 pages, 4 figures, 3 tables, satellite image dataset
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents an aligned multi-temporal and multi-resolution satellite
image dataset for research in change detection. We expect our dataset to be
useful to researchers who want to fuse information from multiple satellites for
detecting changes on the surface of the earth that may not be fully visible in
any single satellite. The dataset we present was created by augmenting the
SpaceNet-7 dataset with temporally parallel stacks of Landsat and Sentinel
images. The SpaceNet-7 dataset consists of time-sequenced Planet images
recorded over 101 AOIs (Areas-of-Interest). In our dataset, for each of the 60
AOIs that are meant for training, we augment the Planet datacube with
temporally parallel datacubes of Landsat and Sentinel images. The temporal
alignments between the high-res Planet images, on the one hand, and the Landsat
and Sentinel images, on the other, are approximate since the temporal
resolution for the Planet images is one month -- each image being a mosaic of
the best data collected over a month. Whenever we have a choice regarding which
Landsat and Sentinel images to pair up with the Planet images, we have chosen
those that had the least cloud cover. A particularly important feature of our
dataset is that the high-res and the low-res images are spatially aligned
together with our MuRA framework presented in this paper. Foundational to the
alignment calculation is the modeling of inter-satellite misalignment errors
with polynomials as in NASA's AROP algorithm. We have named our dataset MuRA-T
for the MuRA framework that is used for aligning the cross-satellite images and
"T" for the temporal dimension in the dataset.
|
[
{
"version": "v1",
"created": "Thu, 23 Feb 2023 19:43:20 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Feb 2023 20:50:27 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Deshmukh",
"Rahul",
""
],
[
"Roros",
"Constantine J.",
""
],
[
"Kashyap",
"Amith",
""
],
[
"Kak",
"Avinash C.",
""
]
] |
new_dataset
| 0.999719 |
2302.12746
|
\'Oscar Garc\'ia-Sierra
|
Miguel Ortega-Mart\'in, \'Oscar Garc\'ia-Sierra, Alfonso Ardoiz, Juan
Carlos Armenteros, Jorge \'Alvarez and Adri\'an Alonso
|
Spanish Built Factual Freectianary (Spanish-BFF): the first AI-generated
free dictionary
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Dictionaries are one of the oldest and most used linguistic resources.
Building them is a complex task that, to the best of our knowledge, has yet to
be explored with generative Large Language Models (LLMs). We introduce the
"Spanish Built Factual Freectianary" (Spanish-BFF) as the first Spanish
AI-generated dictionary. This first-of-its-kind free dictionary uses GPT-3. We
also define future steps we aim to follow to improve this initial commitment to
the field, such as more additional languages.
|
[
{
"version": "v1",
"created": "Fri, 24 Feb 2023 16:59:54 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 17:54:00 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Ortega-Martín",
"Miguel",
""
],
[
"García-Sierra",
"Óscar",
""
],
[
"Ardoiz",
"Alfonso",
""
],
[
"Armenteros",
"Juan Carlos",
""
],
[
"Álvarez",
"Jorge",
""
],
[
"Alonso",
"Adrián",
""
]
] |
new_dataset
| 0.995342 |
2302.12921
|
Maximillian Chen
|
Maximillian Chen, Zhou Yu
|
Pre-Finetuning for Few-Shot Emotional Speech Recognition
|
5 pages, 4 figures. Code available at
https://github.com/maxlchen/Speech-PreFinetuning
| null | null | null |
cs.CL cs.LG cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Speech models have long been known to overfit individual speakers for many
classification tasks. This leads to poor generalization in settings where the
speakers are out-of-domain or out-of-distribution, as is common in production
environments. We view speaker adaptation as a few-shot learning problem and
propose investigating transfer learning approaches inspired by recent success
with pre-trained models in natural language tasks. We propose pre-finetuning
speech models on difficult tasks to distill knowledge into few-shot downstream
classification objectives. We pre-finetune Wav2Vec2.0 on every permutation of
four multiclass emotional speech recognition corpora and evaluate our
pre-finetuned models through 33,600 few-shot fine-tuning trials on the
Emotional Speech Dataset.
|
[
{
"version": "v1",
"created": "Fri, 24 Feb 2023 22:38:54 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 02:28:41 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Chen",
"Maximillian",
""
],
[
"Yu",
"Zhou",
""
]
] |
new_dataset
| 0.95199 |
2302.13506
|
Yu-Tsung Lee
|
Yu-Tsung Lee, Haining Chen, William Enck, Hayawardh Vijayakumar,
Ninghui Li, Zhiyun Qian, Giuseppe Petracca, Trent Jaeger
|
PolyScope: Multi-Policy Access Control Analysis to Triage Android Scoped
Storage
|
14 pages, 5 figures, submitted to IEEE TDSC. arXiv admin note:
substantial text overlap with arXiv:2008.03593
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Android's filesystem access control is a crucial aspect of its system
integrity. It utilizes a combination of mandatory access controls, such as
SELinux, and discretionary access controls, like Unix permissions, along with
specialized access controls such as Android permissions to safeguard OEM and
Android services from third-party applications. However, when OEMs introduce
differentiating features, they often create vulnerabilities due to their
inability to properly reconfigure this complex policy combination. To address
this, we introduce the POLYSCOPE tool, which triages Android filesystem access
control policies to identify attack operations - authorized operations that may
be exploited by adversaries to elevate their privileges. POLYSCOPE has three
significant advantages over prior analyses: it allows for the independent
extension and analysis of individual policy models, understands the flexibility
untrusted parties have in modifying access control policies, and can identify
attack operations that system configurations permit. We demonstrate the
effectiveness of POLYSCOPE by examining the impact of Scoped Storage on
Android, revealing that it reduces the number of attack operations possible on
external storage resources by over 50%. However, because OEMs only partially
adopt Scoped Storage, we also uncover two previously unknown vulnerabilities,
demonstrating how POLYSCOPE can assess an ideal scenario where all apps comply
with Scoped Storage, which can reduce the number of untrusted parties accessing
attack operations by over 65% on OEM systems. POLYSCOPE thus helps Android OEMs
evaluate complex access control policies to pinpoint the attack operations that
require further examination.
|
[
{
"version": "v1",
"created": "Mon, 27 Feb 2023 04:03:23 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Feb 2023 02:10:29 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Lee",
"Yu-Tsung",
""
],
[
"Chen",
"Haining",
""
],
[
"Enck",
"William",
""
],
[
"Vijayakumar",
"Hayawardh",
""
],
[
"Li",
"Ninghui",
""
],
[
"Qian",
"Zhiyun",
""
],
[
"Petracca",
"Giuseppe",
""
],
[
"Jaeger",
"Trent",
""
]
] |
new_dataset
| 0.998095 |
2302.14123
|
Kate Donahue
|
Kate Donahue and Jon Kleinberg
|
Private Blotto: Viewpoint Competition with Polarized Agents
| null | null | null | null |
cs.GT cs.CY cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Colonel Blotto games are one of the oldest settings in game theory,
originally proposed over a century ago in Borel 1921. However, they were
originally designed to model two centrally-controlled armies competing over
zero-sum "fronts", a specific scenario with limited modern-day application. In
this work, we propose and study Private Blotto games, a variant connected to
crowdsourcing and social media. One key difference in Private Blotto is that
individual agents act independently, without being coordinated by a central
"Colonel". This model naturally arises from scenarios such as activist groups
competing over multiple issues, partisan fund-raisers competing over elections
in multiple states, or politically-biased social media users labeling news
articles as misinformation. In this work, we completely characterize the Nash
Stability of the Private Blotto game. Specifically, we show that the outcome
function has a critical impact on the outcome of the game: we study whether a
front is won by majority rule (median outcome) or a smoother outcome taking
into account all agents (mean outcome). We study how this impacts the amount of
"misallocated effort", or agents whose choices doesn't influence the final
outcome. In general, mean outcome ensures that, if a stable arrangement exists,
agents are close to evenly spaced across fronts, minimizing misallocated
effort. However, mean outcome functions also have chaotic patterns as to when
stable arrangements do and do not exist. For median outcome, we exactly
characterize when a stable arrangement exists, but show that this outcome
function frequently results in extremely unbalanced allocation of agents across
fronts.
|
[
{
"version": "v1",
"created": "Mon, 27 Feb 2023 20:12:13 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Donahue",
"Kate",
""
],
[
"Kleinberg",
"Jon",
""
]
] |
new_dataset
| 0.998736 |
2302.14125
|
Manuel Wettstein
|
Bernd G\"artner, Manuel Wettstein
|
A Note on the Faces of the Dual Koch Arrangement
| null | null | null | null |
cs.CG math.CO
|
http://creativecommons.org/licenses/by/4.0/
|
We analyze the faces of the dual Koch arrangement, which is the arrangement
of $2^s + 1$ lines obtained by projective duality from the Koch chain $K_s$. In
particular, we show that this line arrangement does not contain any $k$-gons
for $k > 5$, and that the number of pentagons is $3 \cdot 2^{s-1} - 3$.
|
[
{
"version": "v1",
"created": "Mon, 27 Feb 2023 20:16:42 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Gärtner",
"Bernd",
""
],
[
"Wettstein",
"Manuel",
""
]
] |
new_dataset
| 0.978079 |
2302.14161
|
Yiyuan Lee
|
Yiyuan Lee, Wil Thomason, Zachary Kingston, Lydia E. Kavraki
|
Object Reconfiguration with Simulation-Derived Feasible Actions
|
Appears in IEEE International Conference on Robotics and Automation
(ICRA) 2023
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
3D object reconfiguration encompasses common robot manipulation tasks in
which a set of objects must be moved through a series of physically feasible
state changes into a desired final configuration. Object reconfiguration is
challenging to solve in general, as it requires efficient reasoning about
environment physics that determine action validity. This information is
typically manually encoded in an explicit transition system. Constructing these
explicit encodings is tedious and error-prone, and is often a bottleneck for
planner use. In this work, we explore embedding a physics simulator within a
motion planner to implicitly discover and specify the valid actions from any
state, removing the need for manual specification of action semantics. Our
experiments demonstrate that the resulting simulation-based planner can
effectively produce physically valid rearrangement trajectories for a range of
3D object reconfiguration problems without requiring more than an environment
description and start and goal arrangements.
|
[
{
"version": "v1",
"created": "Mon, 27 Feb 2023 21:48:31 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Lee",
"Yiyuan",
""
],
[
"Thomason",
"Wil",
""
],
[
"Kingston",
"Zachary",
""
],
[
"Kavraki",
"Lydia E.",
""
]
] |
new_dataset
| 0.994358 |
2302.14163
|
Prashant Pandey
|
Prashant Pandey, Mustafa Chasmai, Monish Natarajan, Brejesh Lall
|
A Language-Guided Benchmark for Weakly Supervised Open Vocabulary
Semantic Segmentation
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Increasing attention is being diverted to data-efficient problem settings
like Open Vocabulary Semantic Segmentation (OVSS) which deals with segmenting
an arbitrary object that may or may not be seen during training. The closest
standard problems related to OVSS are Zero-Shot and Few-Shot Segmentation (ZSS,
FSS) and their Cross-dataset variants where zero to few annotations are needed
to segment novel classes. The existing FSS and ZSS methods utilize fully
supervised pixel-labelled seen classes to segment unseen classes. Pixel-level
labels are hard to obtain, and using weak supervision in the form of
inexpensive image-level labels is often more practical. To this end, we propose
a novel unified weakly supervised OVSS pipeline that can perform ZSS, FSS and
Cross-dataset segmentation on novel classes without using pixel-level labels
for either the base (seen) or the novel (unseen) classes in an inductive
setting. We propose Weakly-Supervised Language-Guided Segmentation Network
(WLSegNet), a novel language-guided segmentation pipeline that i) learns
generalizable context vectors with batch aggregates (mean) to map class prompts
to image features using frozen CLIP (a vision-language model) and ii) decouples
weak ZSS/FSS into weak semantic segmentation and Zero-Shot segmentation. The
learned context vectors avoid overfitting on seen classes during training and
transfer better to novel classes during testing. WLSegNet avoids fine-tuning
and the use of external datasets during training. The proposed pipeline beats
existing methods for weak generalized Zero-Shot and weak Few-Shot semantic
segmentation by 39 and 3 mIOU points respectively on PASCAL VOC and weak
Few-Shot semantic segmentation by 5 mIOU points on MS COCO. On a harder setting
of 2-way 1-shot weak FSS, WLSegNet beats the baselines by 13 and 22 mIOU points
on PASCAL VOC and MS COCO, respectively.
|
[
{
"version": "v1",
"created": "Mon, 27 Feb 2023 21:55:48 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Pandey",
"Prashant",
""
],
[
"Chasmai",
"Mustafa",
""
],
[
"Natarajan",
"Monish",
""
],
[
"Lall",
"Brejesh",
""
]
] |
new_dataset
| 0.968443 |
2302.14201
|
Alagappan Ramanathan
|
Alagappan Ramanathan, Sangeetha Abdu Jyothi
|
Nautilus: A Framework for Cross-Layer Cartography of Submarine Cables
and IP Links
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Submarine cables constitute the backbone of the Internet. However, these
critical infrastructure components are vulnerable to several natural and
man-made threats, and during failures, are difficult to repair in their remote
oceanic environments. In spite of their crucial role, we have a limited
understanding of the impact of submarine cable failures on global connectivity,
particularly on the higher layers of the Internet.
In this paper, we present Nautilus, a framework for cross-layer cartography
of submarine cables and IP links. Using a corpus of public datasets and
Internet cartographic techniques, Nautilus identifies IP links that are likely
traversing submarine cables and maps them to one or more potential cables.
Nautilus also gives each IP to cable assignment a prediction score that
reflects the confidence in the mapping. Nautilus generates a mapping for 3.05
million and 1.42 million IPv4 and IPv6 links respectively, covering 91% of all
active cables. In the absence of ground truth data, we validate Nautilus
mapping using three techniques: analyzing past cable failures, using targeted
traceroute measurements, and comparing with public network maps of two
operators.
|
[
{
"version": "v1",
"created": "Mon, 27 Feb 2023 23:35:55 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Ramanathan",
"Alagappan",
""
],
[
"Jyothi",
"Sangeetha Abdu",
""
]
] |
new_dataset
| 0.997517 |
2302.14249
|
Boren Jiang
|
Boren Jiang, Ximeng Tao, Yuanfeng Han, Wanze Li, Gregory S.Chirikjian
|
Model-Free and Learning-Free Proprioceptive Humanoid Movement Control
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a novel model-free method for humanoid-robot quasi-static
movement control. Traditional model-based methods often require precise robot
model parameters. Additionally, existing learning-based frameworks often train
the policy in simulation environments, thereby indirectly relying on a model.
In contrast, we propose a proprioceptive framework based only on sensory
outputs. It does not require prior knowledge of a robot's kinematic model or
inertial parameters. Our method consists of three steps: 1. Planning different
pairs of center of pressure (CoP) and foot position objectives within a single
cycle. 2. Searching around the current configuration by slightly moving the
robot's leg joints back and forth while recording the sensor measurements of
its CoP and foot positions. 3. Updating the robot motion with an optimization
algorithm until all objectives are achieved. We demonstrate our approach on a
NAO humanoid robot platform. Experiment results show that it can successfully
generate stable robot motions.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 02:20:55 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Jiang",
"Boren",
""
],
[
"Tao",
"Ximeng",
""
],
[
"Han",
"Yuanfeng",
""
],
[
"Li",
"Wanze",
""
],
[
"Chirikjian",
"Gregory S.",
""
]
] |
new_dataset
| 0.995148 |
2302.14251
|
Hyomin Kim
|
Hyomin Kim, Hyeonseo Nam, Jungeon Kim, Jaesik Park, and Seungyong Lee
|
LaplacianFusion: Detailed 3D Clothed-Human Body Reconstruction
| null |
ACM Transactions on Graphics (TOG) 41.6 (2022): 1-14
|
10.1145/3550454.3555511
| null |
cs.GR cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
We propose LaplacianFusion, a novel approach that reconstructs detailed and
controllable 3D clothed-human body shapes from an input depth or 3D point cloud
sequence. The key idea of our approach is to use Laplacian coordinates,
well-known differential coordinates that have been used for mesh editing, for
representing the local structures contained in the input scans, instead of
implicit 3D functions or vertex displacements used previously. Our approach
reconstructs a controllable base mesh using SMPL, and learns a surface function
that predicts Laplacian coordinates representing surface details on the base
mesh. For a given pose, we first build and subdivide a base mesh, which is a
deformed SMPL template, and then estimate Laplacian coordinates for the mesh
vertices using the surface function. The final reconstruction for the pose is
obtained by integrating the estimated Laplacian coordinates as a whole.
Experimental results show that our approach based on Laplacian coordinates
successfully reconstructs more visually pleasing shape details than previous
methods. The approach also enables various surface detail manipulations, such
as detail transfer and enhancement.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 02:22:24 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Kim",
"Hyomin",
""
],
[
"Nam",
"Hyeonseo",
""
],
[
"Kim",
"Jungeon",
""
],
[
"Park",
"Jaesik",
""
],
[
"Lee",
"Seungyong",
""
]
] |
new_dataset
| 0.994951 |
2302.14261
|
Xueming Yan
|
Xueming Yan, Zhihang Fang, Yaochu Jin
|
Augmented Transformers with Adaptive n-grams Embedding for Multilingual
Scene Text Recognition
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While vision transformers have been highly successful in improving the
performance in image-based tasks, not much work has been reported on applying
transformers to multilingual scene text recognition due to the complexities in
the visual appearance of multilingual texts. To fill the gap, this paper
proposes an augmented transformer architecture with n-grams embedding and
cross-language rectification (TANGER). TANGER consists of a primary transformer
with single patch embeddings of visual images, and a supplementary transformer
with adaptive n-grams embeddings that aims to flexibly explore the potential
correlations between neighbouring visual patches, which is essential for
feature extraction from multilingual scene texts. Cross-language rectification
is achieved with a loss function that takes into account both language
identification and contextual coherence scoring. Extensive comparative studies
are conducted on four widely used benchmark datasets as well as a new
multilingual scene text dataset containing Indonesian, English, and Chinese
collected from tourism scenes in Indonesia. Our experimental results
demonstrate that TANGER is considerably better compared to the
state-of-the-art, especially in handling complex multilingual scene texts.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 02:37:30 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Yan",
"Xueming",
""
],
[
"Fang",
"Zhihang",
""
],
[
"Jin",
"Yaochu",
""
]
] |
new_dataset
| 0.985596 |
2302.14286
|
Jianing Wang
|
Jianing Wang, Nuo Chen, Qiushi Sun, Wenkang Huang, Chengyu Wang, Ming
Gao
|
HugNLP: A Unified and Comprehensive Library for Natural Language
Processing
|
8 Pages
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we introduce HugNLP, a unified and comprehensive library for
natural language processing (NLP) with the prevalent backend of HuggingFace
Transformers, which is designed for NLP researchers to easily utilize
off-the-shelf algorithms and develop novel methods with user-defined models and
tasks in real-world scenarios. HugNLP consists of a hierarchical structure
including models, processors and applications that unifies the learning process
of pre-trained language models (PLMs) on different NLP tasks. Additionally, we
present some featured NLP applications to show the effectiveness of HugNLP,
such as knowledge-enhanced PLMs, universal information extraction, low-resource
mining, and code understanding and generation, etc. The source code will be
released on GitHub (https://github.com/wjn1996/HugNLP).
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 03:38:26 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Wang",
"Jianing",
""
],
[
"Chen",
"Nuo",
""
],
[
"Sun",
"Qiushi",
""
],
[
"Huang",
"Wenkang",
""
],
[
"Wang",
"Chengyu",
""
],
[
"Gao",
"Ming",
""
]
] |
new_dataset
| 0.997625 |
2302.14298
|
Zikang Yuan
|
Zikang Yuan, Fengtian Lang, Tianle Xu, Xin Yang
|
LIW-OAM: Lidar-Inertial-Wheel Odometry and Mapping
|
8 pages, 3 figures, submit to IROS 2023. arXiv admin note:
substantial text overlap with arXiv:2210.10424
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
LiDAR-inertial odometry and mapping (LIOAM), which fuses complementary
information of a LiDAR and an Inertial Measurement Unit (IMU), is an attractive
solution for pose estimation and mapping. In LI-OAM, both pose and velocity are
regarded as state variables that need to be solved. However, the widely-used
Iterative Closest Point (ICP) algorithm can only provide constraint for pose,
while the velocity can only be constrained by IMU pre-integration. As a result,
the velocity estimates inclined to be updated accordingly with the pose
results. In this paper, we propose LIW-OAM, an accurate and robust
LiDAR-inertial-wheel odometry and mapping system, which fuses the measurements
from LiDAR, IMU and wheel encoder in a bundle adjustment (BA) based
optimization framework. The involvement of a wheel encoder could provide
velocity measurement as an important observation, which assists LI-OAM to
provide a more accurate state prediction. In addition, constraining the
velocity variable by the observation from wheel encoder in optimization can
further improve the accuracy of state estimation. Experiment results on two
public datasets demonstrate that our system outperforms all state-of-the-art
LI-OAM systems in terms of smaller absolute trajectory error (ATE), and
embedding a wheel encoder can greatly improve the performance of LI-OAM based
on the BA framework.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 04:16:21 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Yuan",
"Zikang",
""
],
[
"Lang",
"Fengtian",
""
],
[
"Xu",
"Tianle",
""
],
[
"Yang",
"Xin",
""
]
] |
new_dataset
| 0.999684 |
2302.14306
|
Srikanth Malla
|
Srikanth Malla, Yi-Ting Chen
|
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and
Feature Mapping
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Point cloud data plays an essential role in robotics and self-driving
applications. Yet, annotating point cloud data is time-consuming and nontrivial
while they enable learning discriminative 3D representations that empower
downstream tasks, such as classification and segmentation. Recently,
contrastive learning-based frameworks have shown promising results for learning
3D representations in a self-supervised manner. However, existing contrastive
learning methods cannot precisely encode and associate structural features and
search the higher dimensional augmentation space efficiently. In this paper, we
present CLR-GAM, a novel contrastive learning-based framework with Guided
Augmentation (GA) for efficient dynamic exploration strategy and Guided Feature
Mapping (GFM) for similar structural feature association between augmented
point clouds. We empirically demonstrate that the proposed approach achieves
state-of-the-art performance on both simulated and real-world 3D point cloud
datasets for three different downstream tasks, i.e., 3D point cloud
classification, few-shot learning, and object part segmentation.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 04:38:52 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Malla",
"Srikanth",
""
],
[
"Chen",
"Yi-Ting",
""
]
] |
new_dataset
| 0.988716 |
2302.14331
|
Min-Ha Oh
|
Min-Ha Oh, Young-Hwan Kim, Seung-Min Lee, Gyeong-Seok Hwang, Kyung-Sub
Kim, Jae-Young Bae, Ju-Young Kim, Ju-Yong Lee, Yu-Chan Kim, Sang Yup Kim,
Seung-Kyun Kang
|
Lifetime-configurable soft robots via photodegradable silicone elastomer
composites
|
58 pages, 6 figures, 2 Supplementary Text, 15 Supplementary figures,
1 movie
| null | null | null |
cs.RO cond-mat.mtrl-sci cond-mat.soft
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Developing soft robots that can control their own life-cycle and degrade
on-demand while maintaining hyper-elasticity is a significant research
challenge. On-demand degradable soft robots, which conserve their original
functionality during operation and rapidly degrade under specific external
stimulation, present the opportunity to self-direct the disappearance of
temporary robots. This study proposes soft robots and materials that exhibit
excellent mechanical stretchability and can degrade under ultraviolet (UV)
light by mixing a fluoride-generating diphenyliodonium hexafluorophosphate
(DPI-HFP) with a silicone resin. Spectroscopic analysis revealed the mechanism
of Si-O-Si backbone cleavage using fluoride ion (F-), which was generated from
UV exposed DPI-HFP. Furthermore, photo-differential scanning calorimetry (DSC)
based thermal analysis indicated increased decomposition kinetics at increased
temperatures. Additionally, we demonstrated a robotics application of this
composite by fabricating a gaiting robot. The integration of soft electronics,
including strain sensors, temperature sensors, and photodetectors, expanded the
robotic functionalities. This study provides a simple yet novel strategy for
designing lifecycle mimicking soft robotics that can be applied to reduce soft
robotics waste, explore hazardous areas where retrieval of robots is
impossible, and ensure hardware security with on-demand destructive material
platforms.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 05:54:41 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Oh",
"Min-Ha",
""
],
[
"Kim",
"Young-Hwan",
""
],
[
"Lee",
"Seung-Min",
""
],
[
"Hwang",
"Gyeong-Seok",
""
],
[
"Kim",
"Kyung-Sub",
""
],
[
"Bae",
"Jae-Young",
""
],
[
"Kim",
"Ju-Young",
""
],
[
"Lee",
"Ju-Yong",
""
],
[
"Kim",
"Yu-Chan",
""
],
[
"Kim",
"Sang Yup",
""
],
[
"Kang",
"Seung-Kyun",
""
]
] |
new_dataset
| 0.993558 |
2302.14334
|
Yuyang Chen
|
Yuyang Chen, Dingkang Wang, Lenworth Thomas, Karthik Dantu, Sanjeev J.
Koppal
|
Design of an Adaptive Lightweight LiDAR to Decouple Robot-Camera
Geometry
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
A fundamental challenge in robot perception is the coupling of the sensor
pose and robot pose. This has led to research in active vision where robot pose
is changed to reorient the sensor to areas of interest for perception. Further,
egomotion such as jitter, and external effects such as wind and others affect
perception requiring additional effort in software such as image stabilization.
This effect is particularly pronounced in micro-air vehicles and micro-robots
who typically are lighter and subject to larger jitter but do not have the
computational capability to perform stabilization in real-time. We present a
novel microelectromechanical (MEMS) mirror LiDAR system to change the field of
view of the LiDAR independent of the robot motion. Our design has the potential
for use on small, low-power systems where the expensive components of the LiDAR
can be placed external to the small robot. We show the utility of our approach
in simulation and on prototype hardware mounted on a UAV. We believe that this
LiDAR and its compact movable scanning design provide mechanisms to decouple
robot and sensor geometry allowing us to simplify robot perception. We also
demonstrate examples of motion compensation using IMU and external odometry
feedback in hardware.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 06:03:42 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Chen",
"Yuyang",
""
],
[
"Wang",
"Dingkang",
""
],
[
"Thomas",
"Lenworth",
""
],
[
"Dantu",
"Karthik",
""
],
[
"Koppal",
"Sanjeev J.",
""
]
] |
new_dataset
| 0.998077 |
2302.14418
|
Ji Hou
|
Yu Zhang, Junle Yu, Xiaolin Huang, Wenhui Zhou, Ji Hou
|
PCR-CG: Point Cloud Registration via Deep Color and Geometry
|
accepted to ECCV2022; code at https://github.com/Gardlin/PCR-CG
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we introduce PCR-CG: a novel 3D point cloud registration
module explicitly embedding the color signals into the geometry representation.
Different from previous methods that only use geometry representation, our
module is specifically designed to effectively correlate color into geometry
for the point cloud registration task. Our key contribution is a 2D-3D
cross-modality learning algorithm that embeds the deep features learned from
color signals to the geometry representation. With our designed 2D-3D
projection module, the pixel features in a square region centered at
correspondences perceived from images are effectively correlated with point
clouds. In this way, the overlapped regions can be inferred not only from point
cloud but also from the texture appearances. Adding color is non-trivial. We
compare against a variety of baselines designed for adding color to 3D, such as
exhaustively adding per-pixel features or RGB values in an implicit manner. We
leverage Predator [25] as the baseline method and incorporate our proposed
module onto it. To validate the effectiveness of 2D features, we ablate
different 2D pre-trained networks and show a positive correlation between the
pre-trained weights and the task performance. Our experimental results indicate
a significant improvement of 6.5% registration recall over the baseline method
on the 3DLoMatch benchmark. We additionally evaluate our approach on SOTA
methods and observe consistent improvements, such as an improvement of 2.4%
registration recall over GeoTransformer as well as 3.5% over CoFiNet. Our study
reveals a significant advantages of correlating explicit deep color features to
the point cloud in the registration task.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 08:50:17 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Zhang",
"Yu",
""
],
[
"Yu",
"Junle",
""
],
[
"Huang",
"Xiaolin",
""
],
[
"Zhou",
"Wenhui",
""
],
[
"Hou",
"Ji",
""
]
] |
new_dataset
| 0.998657 |
2302.14475
|
Zhiwu Huang
|
Yabin Wang, Zhiwu Huang, Xiaopeng Hong
|
Benchmarking Deepart Detection
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deepfake technologies have been blurring the boundaries between the real and
unreal, likely resulting in malicious events. By leveraging newly emerged
deepfake technologies, deepfake researchers have been making a great upending
to create deepfake artworks (deeparts), which are further closing the gap
between reality and fantasy. To address potentially appeared ethics questions,
this paper establishes a deepart detection database (DDDB) that consists of a
set of high-quality conventional art images (conarts) and five sets of deepart
images generated by five state-of-the-art deepfake models. This database
enables us to explore once-for-all deepart detection and continual deepart
detection. For the two new problems, we suggest four benchmark evaluations and
four families of solutions on the constructed DDDB. The comprehensive study
demonstrates the effectiveness of the proposed solutions on the established
benchmark dataset, which is capable of paving a way to more interesting
directions of deepart detection. The constructed benchmark dataset and the
source code will be made publicly available.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 10:34:44 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Wang",
"Yabin",
""
],
[
"Huang",
"Zhiwu",
""
],
[
"Hong",
"Xiaopeng",
""
]
] |
new_dataset
| 0.960918 |
2302.14486
|
Gianluca D'Amico Dr
|
Gianluca D'Amico, Mauro Marinoni, Federico Nesti, Giulio Rossolini,
Giorgio Buttazzo, Salvatore Sabina, Gianluigi Lauro
|
TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset
Generation
|
Under review
| null | null | null |
cs.CV cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The railway industry is searching for new ways to automate a number of
complex train functions, such as object detection, track discrimination, and
accurate train positioning, which require the artificial perception of the
railway environment through different types of sensors, including cameras,
LiDARs, wheel encoders, and inertial measurement units. A promising approach
for processing such sensory data is the use of deep learning models, which
proved to achieve excellent performance in other application domains, as
robotics and self-driving cars. However, testing new algorithms and solutions
requires the availability of a large amount of labeled data, acquired in
different scenarios and operating conditions, which are difficult to obtain in
a real railway setting due to strict regulations and practical constraints in
accessing the trackside infrastructure and equipping a train with the required
sensors. To address such difficulties, this paper presents a visual simulation
framework able to generate realistic railway scenarios in a virtual environment
and automatically produce inertial data and labeled datasets from emulated
LiDARs and cameras useful for training deep neural networks or testing
innovative algorithms. A set of experimental results are reported to show the
effectiveness of the proposed approach.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 11:00:13 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"D'Amico",
"Gianluca",
""
],
[
"Marinoni",
"Mauro",
""
],
[
"Nesti",
"Federico",
""
],
[
"Rossolini",
"Giulio",
""
],
[
"Buttazzo",
"Giorgio",
""
],
[
"Sabina",
"Salvatore",
""
],
[
"Lauro",
"Gianluigi",
""
]
] |
new_dataset
| 0.999837 |
2302.14494
|
Elmurod Kuriyozov
|
Elmurod Kuriyozov, Ulugbek Salaev, Sanatbek Matlatipov, Gayrat
Matlatipov
|
Text classification dataset and analysis for Uzbek language
|
Preprint of the paper accepted to The 10th Language & Technology
Conference: Human Language Technologies as a Challenge for Computer Science
and Linguistics. April 21-23, 2023, Poznan, Poland
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Text classification is an important task in Natural Language Processing
(NLP), where the goal is to categorize text data into predefined classes. In
this study, we analyse the dataset creation steps and evaluation techniques of
multi-label news categorisation task as part of text classification. We first
present a newly obtained dataset for Uzbek text classification, which was
collected from 10 different news and press websites and covers 15 categories of
news, press and law texts. We also present a comprehensive evaluation of
different models, ranging from traditional bag-of-words models to deep learning
architectures, on this newly created dataset. Our experiments show that the
Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) based
models outperform the rule-based models. The best performance is achieved by
the BERTbek model, which is a transformer-based BERT model trained on the Uzbek
corpus. Our findings provide a good baseline for further research in Uzbek text
classification.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 11:21:24 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Kuriyozov",
"Elmurod",
""
],
[
"Salaev",
"Ulugbek",
""
],
[
"Matlatipov",
"Sanatbek",
""
],
[
"Matlatipov",
"Gayrat",
""
]
] |
new_dataset
| 0.999601 |
2302.14522
|
Benjamin Sick
|
Benjamin Sick, Michael Walter, Jochen Abhau
|
AdaptiveShape: Solving Shape Variability for 3D Object Detection with
Geometry Aware Anchor Distributions
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
3D object detection with point clouds and images plays an important role in
perception tasks such as autonomous driving. Current methods show great
performance on detection and pose estimation of standard-shaped vehicles but
lack behind on more complex shapes as e.g. semi-trailer truck combinations.
Determining the shape and motion of those special vehicles accurately is
crucial in yard operation and maneuvering and industrial automation
applications. This work introduces several new methods to improve and measure
the performance for such classes. State-of-the-art methods are based on
predefined anchor grids or heatmaps for ground truth targets. However, the
underlying representations do not take the shape of different sized objects
into account. Our main contribution, AdaptiveShape, uses shape aware anchor
distributions and heatmaps to improve the detection capabilities. For large
vehicles we achieve +10.9% AP in comparison to current shape agnostic methods.
Furthermore we introduce a new fast LiDAR-camera fusion. It is based on 2D
bounding box camera detections which are available in many processing
pipelines. This fusion method does not rely on perfectly calibrated or
temporally synchronized systems and is therefore applicable to a broad range of
robotic applications. We extend a standard point pillar network to account for
temporal data and improve learning of complex object movements. In addition we
extended a ground truth augmentation to use grouped object pairs to further
improve truck AP by +2.2% compared to conventional augmentation.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 12:31:31 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Sick",
"Benjamin",
""
],
[
"Walter",
"Michael",
""
],
[
"Abhau",
"Jochen",
""
]
] |
new_dataset
| 0.956508 |
2302.14534
|
Christopher Akiki
|
Christopher Akiki, Odunayo Ogundepo, Aleksandra Piktus, Xinyu Zhang,
Akintunde Oladipo, Jimmy Lin, Martin Potthast
|
Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face
| null | null | null | null |
cs.IR cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present Spacerini, a modular framework for seamless building and
deployment of interactive search applications, designed to facilitate the
qualitative analysis of large scale research datasets. Spacerini integrates
features from both the Pyserini toolkit and the Hugging Face ecosystem to ease
the indexing text collections and deploy them as search engines for ad-hoc
exploration and to make the retrieval of relevant data points quick and
efficient. The user-friendly interface enables searching through massive
datasets in a no-code fashion, making Spacerini broadly accessible to anyone
looking to qualitatively audit their text collections. This is useful both to
IR~researchers aiming to demonstrate the capabilities of their indexes in a
simple and interactive way, and to NLP~researchers looking to better understand
and audit the failure modes of large language models. The framework is open
source and available on GitHub: https://github.com/castorini/hf-spacerini, and
includes utilities to load, pre-process, index, and deploy local and web search
applications. A portfolio of applications created with Spacerini for a
multitude of use cases can be found by visiting https://hf.co/spacerini.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 12:44:10 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Akiki",
"Christopher",
""
],
[
"Ogundepo",
"Odunayo",
""
],
[
"Piktus",
"Aleksandra",
""
],
[
"Zhang",
"Xinyu",
""
],
[
"Oladipo",
"Akintunde",
""
],
[
"Lin",
"Jimmy",
""
],
[
"Potthast",
"Martin",
""
]
] |
new_dataset
| 0.994983 |
2302.14543
|
Himanshu .
|
Himanshu, Jinraj V Pushpangathan and Harikumar Kandath
|
RRT and Velocity Obstacles-based motion planning for Unmanned Aircraft
Systems Traffic Management (UTM)
|
Currently under review in The 2023 International Conference On
Unmanned Aircraft Systems
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, an algorithm for Unmanned Aircraft Systems Traffic Management
(UTM) for a finite number of unmanned aerial vehicles (UAVs) is proposed. This
algorithm is developed by combining the Rapidly-Exploring Random Trees (RRT)
and Velocity Obstacle (VO) algorithms and is referred to as the RRT-VO UTM
algorithm. Here, the RRT algorithm works offline to generate obstacle-free
waypoints in a given environment with known static obstacles. The VO algorithm,
on the other hand, operates online to avoid collisions with other UAVS and
known static obstacles. The boundary of the static obstacles are approximated
by small circles to facilitate the formulation of VO algorithm. The proposed
algorithm's performance is evaluated using numerical simulation and then
compared to the well-known artificial potential field (APF) algorithm for
collision avoidance. The advantages of the proposed method are clearly shown in
terms of lower path length and collision avoidance capabilities for a
challenging scenario.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 13:08:11 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Himanshu",
"",
""
],
[
"Pushpangathan",
"Jinraj V",
""
],
[
"Kandath",
"Harikumar",
""
]
] |
new_dataset
| 0.997389 |
2302.14574
|
Markus Eisenbach
|
Markus Eisenbach, Jannik L\"ubberstedt, Dustin Aganian, Horst-Michael
Gross
|
A Little Bit Attention Is All You Need for Person Re-Identification
|
IEEE International Conference on Robotics and Automation (ICRA) 2023
| null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Person re-identification plays a key role in applications where a mobile
robot needs to track its users over a long period of time, even if they are
partially unobserved for some time, in order to follow them or be available on
demand. In this context, deep-learning based real-time feature extraction on a
mobile robot is often performed on special-purpose devices whose computational
resources are shared for multiple tasks. Therefore, the inference speed has to
be taken into account. In contrast, person re-identification is often improved
by architectural changes that come at the cost of significantly slowing down
inference. Attention blocks are one such example. We will show that some
well-performing attention blocks used in the state of the art are subject to
inference costs that are far too high to justify their use for mobile robotic
applications. As a consequence, we propose an attention block that only
slightly affects the inference speed while keeping up with much deeper networks
or more complex attention blocks in terms of re-identification accuracy. We
perform extensive neural architecture search to derive rules at which locations
this attention block should be integrated into the architecture in order to
achieve the best trade-off between speed and accuracy. Finally, we confirm that
the best performing configuration on a re-identification benchmark also
performs well on an indoor robotic dataset.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 13:54:31 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Eisenbach",
"Markus",
""
],
[
"Lübberstedt",
"Jannik",
""
],
[
"Aganian",
"Dustin",
""
],
[
"Gross",
"Horst-Michael",
""
]
] |
new_dataset
| 0.996827 |
2302.14577
|
Damien Querlioz
|
Kamel-Eddine Harabi, Clement Turck, Marie Drouhin, Adrien Renaudineau,
Thomas Bersani--Veroni, Damien Querlioz, Tifenn Hirtzlin, Elisa Vianello,
Marc Bocquet, Jean-Michel Portal
|
A Multimode Hybrid Memristor-CMOS Prototyping Platform Supporting
Digital and Analog Projects
| null | null |
10.1145/3566097.3567944
| null |
cs.ET
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present an integrated circuit fabricated in a process co-integrating CMOS
and hafnium-oxide memristor technology, which provides a prototyping platform
for projects involving memristors. Our circuit includes the periphery circuitry
for using memristors within digital circuits, as well as an analog mode with
direct access to memristors. The platform allows optimizing the conditions for
reading and writing memristors, as well as developing and testing innovative
memristor-based neuromorphic concepts.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 13:55:42 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Harabi",
"Kamel-Eddine",
""
],
[
"Turck",
"Clement",
""
],
[
"Drouhin",
"Marie",
""
],
[
"Renaudineau",
"Adrien",
""
],
[
"Bersani--Veroni",
"Thomas",
""
],
[
"Querlioz",
"Damien",
""
],
[
"Hirtzlin",
"Tifenn",
""
],
[
"Vianello",
"Elisa",
""
],
[
"Bocquet",
"Marc",
""
],
[
"Portal",
"Jean-Michel",
""
]
] |
new_dataset
| 0.975991 |
2302.14601
|
Sagar Pathrudkar
|
Sagar Pathrudkar, Saadhana Venkataraman, Deepika Kanade, Aswin Ajayan,
Palash Gupta, Shehzaman Khatib, Vijaya Sarathi Indla and Saikat Mukherjee
|
SAFR-AV: Safety Analysis of Autonomous Vehicles using Real World Data --
An end-to-end solution for real world data driven scenario-based testing for
pre-certification of AV stacks
| null | null | null | null |
cs.SE cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
One of the major impediments in deployment of Autonomous Driving Systems
(ADS) is their safety and reliability. The primary reason for the complexity of
testing ADS is that it operates in an open world characterized by its
non-deterministic, high-dimensional and non-stationary nature where the actions
of other actors in the environment are uncontrollable from the ADS's
perspective. This leads to a state space explosion problem and one way of
mitigating this problem is by concretizing the scope for the system under test
(SUT) by testing for a set of behavioral competencies which an ADS must
demonstrate. A popular approach to testing ADS is scenario-based testing where
the ADS is presented with driving scenarios from real world (and synthetically
generated) data and expected to meet defined safety criteria while navigating
through the scenario. We present SAFR-AV, an end-to-end ADS testing platform to
enable scenario-based ADS testing. Our work addresses key real-world challenges
of building an efficient large scale data ingestion pipeline and search
capability to identify scenarios of interest from real world data, creating
digital twins of the real-world scenarios to enable Software-in-the-Loop (SIL)
testing in ADS simulators and, identifying key scenario parameter distributions
to enable optimization of scenario coverage. These along with other modules of
SAFR-AV would allow the platform to provide ADS pre-certifications.
|
[
{
"version": "v1",
"created": "Mon, 27 Feb 2023 11:56:41 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Pathrudkar",
"Sagar",
""
],
[
"Venkataraman",
"Saadhana",
""
],
[
"Kanade",
"Deepika",
""
],
[
"Ajayan",
"Aswin",
""
],
[
"Gupta",
"Palash",
""
],
[
"Khatib",
"Shehzaman",
""
],
[
"Indla",
"Vijaya Sarathi",
""
],
[
"Mukherjee",
"Saikat",
""
]
] |
new_dataset
| 0.985017 |
2302.14624
|
Yooyoung Lee
|
Yooyoung Lee, Craig Greenberg, Eliot Godard, Asad A. Butt, Elliot
Singer, Trang Nguyen, Lisa Mason, Douglas Reynolds
|
The 2022 NIST Language Recognition Evaluation
|
5 pages, 10 figures
| null | null | null |
cs.CL cs.LG cs.SD eess.AS
|
http://creativecommons.org/licenses/by-sa/4.0/
|
In 2022, the U.S. National Institute of Standards and Technology (NIST)
conducted the latest Language Recognition Evaluation (LRE) in an ongoing series
administered by NIST since 1996 to foster research in language recognition and
to measure state-of-the-art technology. Similar to previous LREs, LRE22 focused
on conversational telephone speech (CTS) and broadcast narrowband speech (BNBS)
data. LRE22 also introduced new evaluation features, such as an emphasis on
African languages, including low resource languages, and a test set consisting
of segments containing between 3s and 35s of speech randomly sampled and
extracted from longer recordings. A total of 21 research organizations, forming
16 teams, participated in this 3-month long evaluation and made a total of 65
valid system submissions to be evaluated. This paper presents an overview of
LRE22 and an analysis of system performance over different evaluation
conditions. The evaluation results suggest that Oromo and Tigrinya are easier
to detect while Xhosa and Zulu are more challenging. A greater confusability is
seen for some language pairs. When speech duration increased, system
performance significantly increased up to a certain duration, and then a
diminishing return on system performance is observed afterward.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 15:05:33 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Lee",
"Yooyoung",
""
],
[
"Greenberg",
"Craig",
""
],
[
"Godard",
"Eliot",
""
],
[
"Butt",
"Asad A.",
""
],
[
"Singer",
"Elliot",
""
],
[
"Nguyen",
"Trang",
""
],
[
"Mason",
"Lisa",
""
],
[
"Reynolds",
"Douglas",
""
]
] |
new_dataset
| 0.970035 |
2302.14625
|
Chaitanya Kaul
|
Kevin Mitchell, Khaled Kassem, Chaitanya Kaul, Valentin Kapitany,
Philip Binner, Andrew Ramsay, Roderick Murray-Smith, Daniele Faccio
|
mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor
|
Accepted by ICASSP 2023
| null | null | null |
cs.LG eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
For widespread adoption, public security and surveillance systems must be
accurate, portable, compact, and real-time, without impeding the privacy of the
individuals being observed. Current systems broadly fall into two categories --
image-based which are accurate, but lack privacy, and RF signal-based, which
preserve privacy but lack portability, compactness and accuracy. Our paper
proposes mmSense, an end-to-end portable miniaturised real-time system that can
accurately detect the presence of concealed metallic objects on persons in a
discrete, privacy-preserving modality. mmSense features millimeter wave radar
technology, provided by Google's Soli sensor for its data acquisition, and
TransDope, our real-time neural network, capable of processing a single radar
data frame in 19 ms. mmSense achieves high recognition rates on a diverse set
of challenging scenes while running on standard laptop hardware, demonstrating
a significant advancement towards creating portable, cost-effective real-time
radar based surveillance systems.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 15:06:03 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Mitchell",
"Kevin",
""
],
[
"Kassem",
"Khaled",
""
],
[
"Kaul",
"Chaitanya",
""
],
[
"Kapitany",
"Valentin",
""
],
[
"Binner",
"Philip",
""
],
[
"Ramsay",
"Andrew",
""
],
[
"Murray-Smith",
"Roderick",
""
],
[
"Faccio",
"Daniele",
""
]
] |
new_dataset
| 0.999027 |
2302.14736
|
Yunpeng Bai
|
Yunpeng Bai, Cairong Wang, Shuzhao Xie, Chao Dong, Chun Yuan, Zhi Wang
|
TextIR: A Simple Framework for Text-based Editable Image Restoration
|
9 pages, 8 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most existing image restoration methods use neural networks to learn strong
image-level priors from huge data to estimate the lost information. However,
these works still struggle in cases when images have severe information
deficits. Introducing external priors or using reference images to provide
information also have limitations in the application domain. In contrast, text
input is more readily available and provides information with higher
flexibility. In this work, we design an effective framework that allows the
user to control the restoration process of degraded images with text
descriptions. We use the text-image feature compatibility of the CLIP to
alleviate the difficulty of fusing text and image features. Our framework can
be used for various image restoration tasks, including image inpainting, image
super-resolution, and image colorization. Extensive experiments demonstrate the
effectiveness of our method.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 16:39:36 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Bai",
"Yunpeng",
""
],
[
"Wang",
"Cairong",
""
],
[
"Xie",
"Shuzhao",
""
],
[
"Dong",
"Chao",
""
],
[
"Yuan",
"Chun",
""
],
[
"Wang",
"Zhi",
""
]
] |
new_dataset
| 0.991942 |
2302.14746
|
Ji Hou
|
Ji Hou, Xiaoliang Dai, Zijian He, Angela Dai, Matthias Nie{\ss}ner
|
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors
|
accepted to CVPR2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current popular backbones in computer vision, such as Vision Transformers
(ViT) and ResNets are trained to perceive the world from 2D images. However, to
more effectively understand 3D structural priors in 2D backbones, we propose
Mask3D to leverage existing large-scale RGB-D data in a self-supervised
pre-training to embed these 3D priors into 2D learned feature representations.
In contrast to traditional 3D contrastive learning paradigms requiring 3D
reconstructions or multi-view correspondences, our approach is simple: we
formulate a pre-text reconstruction task by masking RGB and depth patches in
individual RGB-D frames. We demonstrate the Mask3D is particularly effective in
embedding 3D priors into the powerful 2D ViT backbone, enabling improved
representation learning for various scene understanding tasks, such as semantic
segmentation, instance segmentation and object detection. Experiments show that
Mask3D notably outperforms existing self-supervised 3D pre-training approaches
on ScanNet, NYUv2, and Cityscapes image understanding tasks, with an
improvement of +6.5% mIoU against the state-of-the-art Pri3D on ScanNet image
semantic segmentation.
|
[
{
"version": "v1",
"created": "Tue, 28 Feb 2023 16:45:21 GMT"
}
] | 2023-03-01T00:00:00 |
[
[
"Hou",
"Ji",
""
],
[
"Dai",
"Xiaoliang",
""
],
[
"He",
"Zijian",
""
],
[
"Dai",
"Angela",
""
],
[
"Nießner",
"Matthias",
""
]
] |
new_dataset
| 0.978232 |
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