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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2207.11605
|
Seyed Ehsan Marjani Bajestani
|
Seyed Ehsan Marjani Bajestani, Giovanni Beltrame
|
Event-based RGB-D sensing with structured light
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Event-based cameras (ECs) are bio-inspired sensors that asynchronously report
brightness changes for each pixel. Due to their high dynamic range, pixel
bandwidth, temporal resolution, low power consumption, and computational
simplicity, they are beneficial for vision-based projects in challenging
lighting conditions and they can detect fast movements with their microsecond
response time. The first generation of ECs are monochrome, but color data is
very useful and sometimes essential for certain vision-based applications. The
latest technology enables manufacturers to build color ECs, trading off the
size of the sensor and substantially reducing the resolution compared to
monochrome models, despite having the same bandwidth. In addition, ECs only
detect changes in light and do not show static or slowly moving objects. We
introduce a method to detect full RGB events using a monochrome EC aided by a
structured light projector. The projector emits rapidly changing RGB patterns
of light beams on the scene, the reflection of which is captured by the EC. We
combine the benefits of ECs and projection-based techniques and allow depth and
color detection of static or moving objects with a commercial TI LightCrafter
4500 projector and a monocular monochrome EC, paving the way for frameless
RGB-D sensing applications.
|
[
{
"version": "v1",
"created": "Sat, 23 Jul 2022 21:10:01 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Bajestani",
"Seyed Ehsan Marjani",
""
],
[
"Beltrame",
"Giovanni",
""
]
] |
new_dataset
| 0.988087 |
2207.11689
|
Pengfei Qiu
|
Pengfei Qiu, Yongqiang Lyu, Haixia Wang, Dongsheng Wang, Chang Liu,
Qiang Gao, Chunlu Wang, Rihui Sun, Gang Qu
|
PMUSpill: The Counters in Performance Monitor Unit that Leak
SGX-Protected Secrets
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Performance Monitor Unit (PMU) is a significant hardware module on the
current processors, which counts the events launched by processor into a set of
PMU counters. Ideally, the events triggered by instructions that are executed
but the results are not successfully committed (transient execution) should not
be recorded. However, in this study, we discover that some PMU events triggered
by the transient execution instructions will actually be recorded by PMU. Based
on this, we propose the PMUSpill attack, which enables attackers to maliciously
leak the secret data that are loaded during transient executions. The biggest
challenge is how to encode the secret data into PMU events. We construct an
instruction gadget to solve this challenge, whose execution path that can be
identified by PMU counters represents what values the secret data are. We
successfully implement the PMUSpill attack to leak the secret data stored in
Intel Software Guard Extensions (SGX) (a Trusted Execution Environment (TEE) in
the Intel's processors) through real experiments. Besides, we locate the
vulnerable PMU counters and their trigger instructions by iterating all the
valid PMU counters and instructions. The experiment results demonstrate that
there are up to 20 PMU counters available to implement the PMUSpill attack. We
also provide some possible hardware and software-based countermeasures for
addressing the PMUSpill attack, which can be utilized to enhance the security
of processors in future.
|
[
{
"version": "v1",
"created": "Sun, 24 Jul 2022 08:18:46 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Qiu",
"Pengfei",
""
],
[
"Lyu",
"Yongqiang",
""
],
[
"Wang",
"Haixia",
""
],
[
"Wang",
"Dongsheng",
""
],
[
"Liu",
"Chang",
""
],
[
"Gao",
"Qiang",
""
],
[
"Wang",
"Chunlu",
""
],
[
"Sun",
"Rihui",
""
],
[
"Qu",
"Gang",
""
]
] |
new_dataset
| 0.999528 |
2207.11730
|
Praveen Kumar
|
Praveen Kumar, Sudhan Majhi, Subhabrata Paul
|
A Direct Construction of Cross Z-Complementary Sets with Flexible
Lengths and Large Zero Correlation Zone
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
This letter proposes a direct construction for cross Z-complementary sets
(CZCSs) with flexible lengths and a large zero correlation zone (ZCZ). CZCS is
an extension of the cross Z-complementary pair (CZCP). The maximum possible ZCZ
width of a CZCP is half of its sequence length. In this letter, for the first
time, a generalized Boolean function based construction of CZCSs with a large
number of constituent sequences and a ZCZ ratio of $2/3$ is presented. For
integers $m$ and $\delta$, the proposed construction produces CZCS with length
expressed as $2^{m-1}+2^\delta$ ($0 \leq \delta <m-1,m\geq 4$), where both odd
and even lengths CZCS can be obtained. Additionally, the constructed CZCS also
feature a complementary set of the same length. Finally, the proposed
construction is compared with the existing works.
|
[
{
"version": "v1",
"created": "Sun, 24 Jul 2022 12:22:11 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Kumar",
"Praveen",
""
],
[
"Majhi",
"Sudhan",
""
],
[
"Paul",
"Subhabrata",
""
]
] |
new_dataset
| 0.962025 |
2207.11754
|
Daniel Eckhoff
|
Daniel Eckhoff, Royce Ng, Alvaro Cassinelli
|
Virtual Reality Therapy for the Psychological Well-being of Palliative
Care Patients in Hong Kong
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this paper we introduce novel Virtual Reality (VR) and Augmented Reality
(AR) treatments to improve the psychological well being of patients in
palliative care, based on interviews with a clinical psychologist who has
successfully implemented VR assisted interventions on palliative care patients
in the Hong Kong hospital system. Our VR and AR assisted interventions are
adaptations of traditional palliative care therapies which simultaneously
facilitate patients communication with family and friends while isolated in
hospital due to physical weakness and COVID-19 related restrictions. The first
system we propose is a networked, metaverse platform for palliative care
patients to create customized virtual environments with therapists, family and
friends which function as immersive and collaborative versions of 'life review'
and 'reminiscence therapy'. The second proposed system will investigate the use
of Mixed Reality telepresence and haptic touch in an AR environment, which will
allow palliative care patients to physically feel friends and family in a
virtual space, adding to the sense of presence and immersion in that
environment.
|
[
{
"version": "v1",
"created": "Sun, 24 Jul 2022 14:31:52 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Eckhoff",
"Daniel",
""
],
[
"Ng",
"Royce",
""
],
[
"Cassinelli",
"Alvaro",
""
]
] |
new_dataset
| 0.993775 |
2207.11765
|
Jose Cambronero Sanchez
|
Rohan Bavishi, Harshit Joshi, Jos\'e Pablo Cambronero S\'anchez, Anna
Fariha, Sumit Gulwani, Vu Le, Ivan Radicek, Ashish Tiwari
|
Neurosymbolic Repair for Low-Code Formula Languages
| null | null | null | null |
cs.SE cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Most users of low-code platforms, such as Excel and PowerApps, write programs
in domain-specific formula languages to carry out nontrivial tasks. Often users
can write most of the program they want, but introduce small mistakes that
yield broken formulas. These mistakes, which can be both syntactic and
semantic, are hard for low-code users to identify and fix, even though they can
be resolved with just a few edits. We formalize the problem of producing such
edits as the last-mile repair problem. To address this problem, we developed
LaMirage, a LAst-MIle RepAir-engine GEnerator that combines symbolic and neural
techniques to perform last-mile repair in low-code formula languages. LaMirage
takes a grammar and a set of domain-specific constraints/rules, which jointly
approximate the target language, and uses these to generate a repair engine
that can fix formulas in that language. To tackle the challenges of localizing
the errors and ranking the candidate repairs, LaMirage leverages neural
techniques, whereas it relies on symbolic methods to generate candidate
repairs. This combination allows LaMirage to find repairs that satisfy the
provided grammar and constraints, and then pick the most natural repair. We
compare LaMirage to state-of-the-art neural and symbolic approaches on 400 real
Excel and PowerFx formulas, where LaMirage outperforms all baselines. We
release these benchmarks to encourage subsequent work in low-code domains.
|
[
{
"version": "v1",
"created": "Sun, 24 Jul 2022 15:56:03 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Bavishi",
"Rohan",
""
],
[
"Joshi",
"Harshit",
""
],
[
"Sánchez",
"José Pablo Cambronero",
""
],
[
"Fariha",
"Anna",
""
],
[
"Gulwani",
"Sumit",
""
],
[
"Le",
"Vu",
""
],
[
"Radicek",
"Ivan",
""
],
[
"Tiwari",
"Ashish",
""
]
] |
new_dataset
| 0.986755 |
2207.11795
|
Zezhou Cheng
|
Zezhou Cheng, Menglei Chai, Jian Ren, Hsin-Ying Lee, Kyle Olszewski,
Zeng Huang, Subhransu Maji, Sergey Tulyakov
|
Cross-Modal 3D Shape Generation and Manipulation
|
ECCV 2022. Project page:
https://people.cs.umass.edu/~zezhoucheng/edit3d/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Creating and editing the shape and color of 3D objects require tremendous
human effort and expertise. Compared to direct manipulation in 3D interfaces,
2D interactions such as sketches and scribbles are usually much more natural
and intuitive for the users. In this paper, we propose a generic multi-modal
generative model that couples the 2D modalities and implicit 3D representations
through shared latent spaces. With the proposed model, versatile 3D generation
and manipulation are enabled by simply propagating the editing from a specific
2D controlling modality through the latent spaces. For example, editing the 3D
shape by drawing a sketch, re-colorizing the 3D surface via painting color
scribbles on the 2D rendering, or generating 3D shapes of a certain category
given one or a few reference images. Unlike prior works, our model does not
require re-training or fine-tuning per editing task and is also conceptually
simple, easy to implement, robust to input domain shifts, and flexible to
diverse reconstruction on partial 2D inputs. We evaluate our framework on two
representative 2D modalities of grayscale line sketches and rendered color
images, and demonstrate that our method enables various shape manipulation and
generation tasks with these 2D modalities.
|
[
{
"version": "v1",
"created": "Sun, 24 Jul 2022 19:22:57 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Cheng",
"Zezhou",
""
],
[
"Chai",
"Menglei",
""
],
[
"Ren",
"Jian",
""
],
[
"Lee",
"Hsin-Ying",
""
],
[
"Olszewski",
"Kyle",
""
],
[
"Huang",
"Zeng",
""
],
[
"Maji",
"Subhransu",
""
],
[
"Tulyakov",
"Sergey",
""
]
] |
new_dataset
| 0.980588 |
2207.11808
|
Hossein Mirzaee
|
Hossein Mirzaee (1), Javad Peymanfard (2), Hamid Habibzadeh Moshtaghin
(3), Hossein Zeinali (1) ((1) Amirkabir University of Technology, (2) Iran
University of Science and Technology, (3) Allameh Tabataba'i University)
|
ArmanEmo: A Persian Dataset for Text-based Emotion Detection
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
With the recent proliferation of open textual data on social media platforms,
Emotion Detection (ED) from Text has received more attention over the past
years. It has many applications, especially for businesses and online service
providers, where emotion detection techniques can help them make informed
commercial decisions by analyzing customers/users' feelings towards their
products and services. In this study, we introduce ArmanEmo, a human-labeled
emotion dataset of more than 7000 Persian sentences labeled for seven
categories. The dataset has been collected from different resources, including
Twitter, Instagram, and Digikala (an Iranian e-commerce company) comments.
Labels are based on Ekman's six basic emotions (Anger, Fear, Happiness, Hatred,
Sadness, Wonder) and another category (Other) to consider any other emotion not
included in Ekman's model. Along with the dataset, we have provided several
baseline models for emotion classification focusing on the state-of-the-art
transformer-based language models. Our best model achieves a macro-averaged F1
score of 75.39 percent across our test dataset. Moreover, we also conduct
transfer learning experiments to compare our proposed dataset's generalization
against other Persian emotion datasets. Results of these experiments suggest
that our dataset has superior generalizability among the existing Persian
emotion datasets. ArmanEmo is publicly available for non-commercial use at
https://github.com/Arman-Rayan-Sharif/arman-text-emotion.
|
[
{
"version": "v1",
"created": "Sun, 24 Jul 2022 20:35:23 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Mirzaee",
"Hossein",
""
],
[
"Peymanfard",
"Javad",
""
],
[
"Moshtaghin",
"Hamid Habibzadeh",
""
],
[
"Zeinali",
"Hossein",
""
]
] |
new_dataset
| 0.999909 |
2207.11810
|
Alexander Bell
|
Yu-Yun Tseng, Alexander Bell, and Danna Gurari
|
VizWiz-FewShot: Locating Objects in Images Taken by People With Visual
Impairments
|
Accepted to ECCV 2022. The first two authors contributed equally
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce a few-shot localization dataset originating from photographers
who authentically were trying to learn about the visual content in the images
they took. It includes nearly 10,000 segmentations of 100 categories in over
4,500 images that were taken by people with visual impairments. Compared to
existing few-shot object detection and instance segmentation datasets, our
dataset is the first to locate holes in objects (e.g., found in 12.3\% of our
segmentations), it shows objects that occupy a much larger range of sizes
relative to the images, and text is over five times more common in our objects
(e.g., found in 22.4\% of our segmentations). Analysis of three modern few-shot
localization algorithms demonstrates that they generalize poorly to our new
dataset. The algorithms commonly struggle to locate objects with holes, very
small and very large objects, and objects lacking text. To encourage a larger
community to work on these unsolved challenges, we publicly share our annotated
few-shot dataset at https://vizwiz.org .
|
[
{
"version": "v1",
"created": "Sun, 24 Jul 2022 20:44:51 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Tseng",
"Yu-Yun",
""
],
[
"Bell",
"Alexander",
""
],
[
"Gurari",
"Danna",
""
]
] |
new_dataset
| 0.999649 |
2207.11817
|
Tu Nguyen
|
Tu N. Nguyen, Kashyab J. Ambarani, Linh Le, Ivan Djordjevic, and
Zhi-Li Zhang
|
A Multiple-Entanglement Routing Framework for Quantum Networks
|
11 pages
| null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Quantum networks are gaining momentum in finding applications in a wide range
of domains. However, little research has investigated the potential of a
quantum network framework to enable highly reliable communications. The goal of
this work is to investigate and design the multiple-entanglement routing
framework, namely k-entangled routing. In particular, the $k$-entangled routing
will enable k paths connecting all demands (source-destination pairs) in the
network. To design the $k$-entangled routing, we propose two algorithms that
are called Sequential Multi-path Scheduling Algorithm and Min-Cut-based
Multi-path Scheduling Algorithm. In addition, we evaluate the performance of
the proposed algorithms and models through a realistic quantum network
simulator, NetSquid, that models the stochastic processes underlying quantum
communications. The results show that the proposed algorithms (SMPSA and MCSA)
largely enhance the network's traffic flexibility. The proposed paradigms would
lay the foundation for further research on the area of entanglement routing.
|
[
{
"version": "v1",
"created": "Tue, 19 Jul 2022 12:09:03 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Nguyen",
"Tu N.",
""
],
[
"Ambarani",
"Kashyab J.",
""
],
[
"Le",
"Linh",
""
],
[
"Djordjevic",
"Ivan",
""
],
[
"Zhang",
"Zhi-Li",
""
]
] |
new_dataset
| 0.991592 |
2207.11857
|
Devdeep Ray
|
Devdeep Ray (1 and 2), Connor Smith (1), Teng Wei (1), David Chu (1),
Srinivasan Seshan (2) ((1) Google, (2) Carnegie Mellon University)
|
SQP: Congestion Control for Low-Latency Interactive Video Streaming
|
14 pages, 2 page appendix
| null | null | null |
cs.NI cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents the design and evaluation of SQP, a congestion control
algorithm (CCA) for interactive video streaming applications that need to
stream high-bitrate compressed video with very low end-to-end frame delay (eg.
AR streaming, cloud gaming). SQP uses frame-coupled, paced packet trains to
sample the network bandwidth, and uses an adaptive one-way delay measurement to
recover from queuing, for low, bounded queuing delay. SQP rapidly adapts to
changes in the link bandwidth, ensuring high utilization and low frame delay,
and also achieves competitive bandwidth shares when competing with
queue-building flows within an acceptable delay envelope. SQP has good fairness
properties, and works well on links with shallow buffers.
In real-world A/B testing of SQP against Copa in Google's AR streaming
platform, SQP achieves 27% and 15% more sessions with high bitrate and low
frame delay for LTE and Wi-Fi, respectively. When competing with queue-building
traffic like Cubic and BBR, SQP achieves 2-3X higher bandwidth compared to
GoogCC (WebRTC), Sprout, and PCC-Vivace, and comparable performance to Copa
(with mode switching).
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 00:37:19 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Ray",
"Devdeep",
"",
"1 and 2"
],
[
"Smith",
"Connor",
"",
"Google"
],
[
"Wei",
"Teng",
"",
"Google"
],
[
"Chu",
"David",
"",
"Google"
],
[
"Seshan",
"Srinivasan",
"",
"Carnegie Mellon University"
]
] |
new_dataset
| 0.997944 |
2207.11889
|
Songlin Fan
|
Songlin Fan, Wei Gao, and Ge Li
|
Salient Object Detection for Point Clouds
|
Accepted to ECCV 2022. Project Page:
https://git.openi.org.cn/OpenPointCloud/PCSOD
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper researches the unexplored task-point cloud salient object
detection (SOD). Differing from SOD for images, we find the attention shift of
point clouds may provoke saliency conflict, i.e., an object paradoxically
belongs to salient and non-salient categories. To eschew this issue, we present
a novel view-dependent perspective of salient objects, reasonably reflecting
the most eye-catching objects in point cloud scenarios. Following this
formulation, we introduce PCSOD, the first dataset proposed for point cloud SOD
consisting of 2,872 in-/out-door 3D views. The samples in our dataset are
labeled with hierarchical annotations, e.g., super-/sub-class, bounding box,
and segmentation map, which endows the brilliant generalizability and broad
applicability of our dataset verifying various conjectures. To evidence the
feasibility of our solution, we further contribute a baseline model and
benchmark five representative models for a comprehensive comparison. The
proposed model can effectively analyze irregular and unordered points for
detecting salient objects. Thanks to incorporating the task-tailored designs,
our method shows visible superiority over other baselines, producing more
satisfactory results. Extensive experiments and discussions reveal the
promising potential of this research field, paving the way for further study.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 03:35:46 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Fan",
"Songlin",
""
],
[
"Gao",
"Wei",
""
],
[
"Li",
"Ge",
""
]
] |
new_dataset
| 0.99972 |
2207.11897
|
Tosin Ige
|
Tosin Ige, Sikiru Adewale
|
AI Powered Anti-Cyber Bullying System using Machine Learning Algorithm
of Multinomial Naive Bayes and Optimized Linear Support Vector Machine
|
5 pages
|
International Journal of Advanced Computer Science and
Applications(IJACSA), Volume 13 Issue 5, 2022
|
10.14569/IJACSA.2022.0130502
| null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
"Unless and until our society recognizes cyber bullying for what it is, the
suffering of thousands of silent victims will continue." ~ Anna Maria Chavez.
There had been series of research on cyber bullying which are unable to provide
reliable solution to cyber bullying. In this research work, we were able to
provide a permanent solution to this by developing a model capable of detecting
and intercepting bullying incoming and outgoing messages with 92% accuracy. We
also developed a chatbot automation messaging system to test our model leading
to the development of Artificial Intelligence powered anti-cyber bullying
system using machine learning algorithm of Multinomial Naive Bayes (MNB) and
optimized linear Support Vector Machine (SVM). Our model is able to detect and
intercept bullying outgoing and incoming bullying messages and take immediate
action.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 04:02:02 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Ige",
"Tosin",
""
],
[
"Adewale",
"Sikiru",
""
]
] |
new_dataset
| 0.970923 |
2207.11911
|
Bangbang Yang
|
Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng
Cui, Guofeng Zhang
|
NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for
Geometry and Texture Editing
|
Accepted to ECCV 2022 (Oral). Project Page:
https://zju3dv.github.io/neumesh/
| null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
Very recently neural implicit rendering techniques have been rapidly evolved
and shown great advantages in novel view synthesis and 3D scene reconstruction.
However, existing neural rendering methods for editing purposes offer limited
functionality, e.g., rigid transformation, or not applicable for fine-grained
editing for general objects from daily lives. In this paper, we present a novel
mesh-based representation by encoding the neural implicit field with
disentangled geometry and texture codes on mesh vertices, which facilitates a
set of editing functionalities, including mesh-guided geometry editing,
designated texture editing with texture swapping, filling and painting
operations. To this end, we develop several techniques including learnable sign
indicators to magnify spatial distinguishability of mesh-based representation,
distillation and fine-tuning mechanism to make a steady convergence, and the
spatial-aware optimization strategy to realize precise texture editing.
Extensive experiments and editing examples on both real and synthetic data
demonstrate the superiority of our method on representation quality and editing
ability. Code is available on the project webpage:
https://zju3dv.github.io/neumesh/.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 05:30:50 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Yang",
"Bangbang",
""
],
[
"Bao",
"Chong",
""
],
[
"Zeng",
"Junyi",
""
],
[
"Bao",
"Hujun",
""
],
[
"Zhang",
"Yinda",
""
],
[
"Cui",
"Zhaopeng",
""
],
[
"Zhang",
"Guofeng",
""
]
] |
new_dataset
| 0.99944 |
2207.11929
|
Irit Dinur
|
Irit Dinur, Shai Evra, Ron Livne, Alexander Lubotzky, Shahar Mozes
|
Good Locally Testable Codes
|
This is a revision of arxiv.org/2111.04808 that has been adapted to a
mathematical audience
| null | null | null |
cs.IT math.CO math.GR math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
An explicit construction of locally testable codes of constant rate, constant
distance and constant number of queries is given. Hence answering affirmatively
the $c^3$-problem.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 06:45:45 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Dinur",
"Irit",
""
],
[
"Evra",
"Shai",
""
],
[
"Livne",
"Ron",
""
],
[
"Lubotzky",
"Alexander",
""
],
[
"Mozes",
"Shahar",
""
]
] |
new_dataset
| 0.991289 |
2207.11936
|
Sergio Barrachina-Mu\~noz Dr
|
Sergio Barrachina-Mu\~noz, Miquel Payar\'o, Josep Mangues-Bafalluy
|
Cloud-native 5G experimental platform with over-the-air transmissions
and end-to-end monitoring
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
5G represents a revolutionary shift with respect to previous generations
given its design centered on network softwarization. Within such a change of
paradigm, cloud-native solutions are widely regarded as the future of vertical
application development because of their enhanced flexibility and adaptability
to complex and dynamic scenarios. In this context, we present an experimental
framework with over-the-air transmissions that tackles two critical aspects for
enhancing the lifecycle management of 5G and beyond networks: cloud-native
deployments of 5G core network functions (NFs) and end-to-end monitoring.
First, we deploy Open5GS and Prometheus-based monitoring as containerized
network functions (CNFs) in a Kubernetes cluster spanning a multi-tier network
with a multi-access edge computing (MEC) host. We then demonstrate the
end-to-end monitoring system by showcasing via Grafana dashboards both
infrastructure resources and radio metrics of two scenarios; one devoted to
user plane function (UPF) re-selection and the other to user mobility.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 07:01:05 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Barrachina-Muñoz",
"Sergio",
""
],
[
"Payaró",
"Miquel",
""
],
[
"Mangues-Bafalluy",
"Josep",
""
]
] |
new_dataset
| 0.989519 |
2207.11972
|
Jian Zhang
|
Chong Mou, Jian Zhang
|
TransCL: Transformer Makes Strong and Flexible Compressive Learning
|
Accepted by TPAMI 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Compressive learning (CL) is an emerging framework that integrates signal
acquisition via compressed sensing (CS) and machine learning for inference
tasks directly on a small number of measurements. It can be a promising
alternative to classical image-domain methods and enjoys great advantages in
memory saving and computational efficiency. However, previous attempts on CL
are not only limited to a fixed CS ratio, which lacks flexibility, but also
limited to MNIST/CIFAR-like datasets and do not scale to complex real-world
high-resolution (HR) data or vision tasks. In this paper, a novel
transformer-based compressive learning framework on large-scale images with
arbitrary CS ratios, dubbed TransCL, is proposed. Specifically, TransCL first
utilizes the strategy of learnable block-based compressed sensing and proposes
a flexible linear projection strategy to enable CL to be performed on
large-scale images in an efficient block-by-block manner with arbitrary CS
ratios. Then, regarding CS measurements from all blocks as a sequence, a pure
transformer-based backbone is deployed to perform vision tasks with various
task-oriented heads. Our sufficient analysis presents that TransCL exhibits
strong resistance to interference and robust adaptability to arbitrary CS
ratios. Extensive experiments for complex HR data demonstrate that the proposed
TransCL can achieve state-of-the-art performance in image classification and
semantic segmentation tasks. In particular, TransCL with a CS ratio of $10\%$
can obtain almost the same performance as when operating directly on the
original data and can still obtain satisfying performance even with an
extremely low CS ratio of $1\%$. The source codes of our proposed TransCL is
available at \url{https://github.com/MC-E/TransCL/}.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 08:21:48 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Mou",
"Chong",
""
],
[
"Zhang",
"Jian",
""
]
] |
new_dataset
| 0.999239 |
2207.12039
|
Ciar\'an Dunne
|
Ciar\'an Dunne and J. B. Wells
|
Isabelle/HOL/GST: A Formal Proof Environment for Generalized Set
Theories
| null | null | null | null |
cs.LO math.LO
|
http://creativecommons.org/licenses/by/4.0/
|
A generalized set theory (GST) is like a standard set theory but also can
have non-set structured objects that can contain other structured objects
including sets. This paper presents Isabelle/HOL support for GSTs, which are
treated as type classes that combine features that specify kinds of
mathematical objects, e.g., sets, ordinal numbers, functions, etc. GSTs can
have an exception feature that eases representing partial functions and
undefinedness. When assembling a GST, extra axioms are generated following a
user-modifiable policy to fill specification gaps. Specialized type-like
predicates called soft types are used extensively. Although a GST can be used
without a model, for confidence in its consistency we build a model for each
GST from components that specify each feature's contribution to each tier of a
von-Neumann-style cumulative hierarchy defined via ordinal recursion, and we
then connect the model to a separate type which the GST occupies.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 10:27:15 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Dunne",
"Ciarán",
""
],
[
"Wells",
"J. B.",
""
]
] |
new_dataset
| 0.956365 |
2207.12063
|
Payam Zahadat
|
Payam Zahadat and Ada Diaconescu
|
Multi-Scale Asset Distribution Model for Dynamic Environments
| null | null | null | null |
cs.MA cs.AI cs.SI cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In many self-organising systems the ability to extract necessary resources
from the external environment is essential to the system's growth and survival.
Examples include the extraction of sunlight and nutrients in organic plants, of
monetary income in business organisations and of mobile robots in swarm
intelligence actions. When operating within competitive, ever-changing
environments, such systems must distribute their internal assets wisely so as
to improve and adapt their ability to extract available resources. As the
system size increases, the asset-distribution process often gets organised
around a multi-scale control topology. This topology may be static (fixed) or
dynamic (enabling growth and structural adaptation) depending on the system's
internal constraints and adaptive mechanisms. In this paper, we expand on a
plant-inspired asset-distribution model and introduce a more general
multi-scale model applicable across a wider range of natural and artificial
system domains. We study the impact that the topology of the multi-scale
control process has upon the system's ability to self-adapt asset distribution
when resource availability changes within the environment. Results show how
different topological characteristics and different competition levels between
system branches impact overall system profitability, adaptation delays and
disturbances when environmental changes occur. These findings provide a basis
for system designers to select the most suitable topology and configuration for
their particular application and execution environment.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 11:14:49 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Zahadat",
"Payam",
""
],
[
"Diaconescu",
"Ada",
""
]
] |
new_dataset
| 0.975249 |
2207.12084
|
Joao P. A. Dantas
|
Joao P. A. Dantas, Andre N. Costa, Vitor C. F. Gomes, Andre R.
Kuroswiski, Felipe L. L. Medeiros and Diego Geraldo
|
ASA: A Simulation Environment for Evaluating Military Operational
Scenarios
| null | null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Aerospace Simulation Environment (Ambiente de Simula\c{c}\~ao
Aeroespacial -- ASA in Portuguese) is a custom-made object-oriented simulation
framework developed mainly in C++ that enables the modeling and simulation of
military operational scenarios to support the development of tactics and
procedures in the aerospace context for the Brazilian Air Force. This work
describes the ASA framework, bringing its distributed architecture for managing
multiple simulation machines, a data analysis platform for post-processing
simulation data, the capability of loading models at simulation runtime, and a
batch mode execution platform to perform multiple independent executions
simultaneously. In addition, we present a list of recent works using the ASA
framework as a simulation tool in the air combat context.
|
[
{
"version": "v1",
"created": "Thu, 23 Jun 2022 15:05:30 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Dantas",
"Joao P. A.",
""
],
[
"Costa",
"Andre N.",
""
],
[
"Gomes",
"Vitor C. F.",
""
],
[
"Kuroswiski",
"Andre R.",
""
],
[
"Medeiros",
"Felipe L. L.",
""
],
[
"Geraldo",
"Diego",
""
]
] |
new_dataset
| 0.999145 |
2207.12162
|
Maxime Amblard
|
Maria Boritchev (SEMAGRAMME, LORIA), Maxime Amblard (SEMAGRAMME,
LORIA)
|
A Multi-Party Dialogue Ressource in French
| null |
13th Edition of Language Resources and Evaluation Conference (LREC
2022), Jun 2022, Marseille, France
| null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present Dialogues in Games (DinG), a corpus of manual transcriptions of
real-life, oral, spontaneous multi-party dialogues between French-speaking
players of the board game Catan. Our objective is to make available a quality
resource for French, composed of long dialogues, to facilitate their study in
the style of (Asher et al., 2016). In a general dialogue setting, participants
share personal information, which makes it impossible to disseminate the
resource freely and openly. In DinG, the attention of the participants is
focused on the game, which prevents them from talking about themselves. In
addition, we are conducting a study on the nature of the questions in dialogue,
through annotation (Cruz Blandon et al., 2019), in order to develop more
natural automatic dialogue systems.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 13:02:54 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Boritchev",
"Maria",
"",
"SEMAGRAMME, LORIA"
],
[
"Amblard",
"Maxime",
"",
"SEMAGRAMME,\n LORIA"
]
] |
new_dataset
| 0.999489 |
2207.12188
|
Che-Kai Liu
|
Che-Kai Liu, Haobang Chen, Mohsen Imani, Kai Ni, Arman Kazemi, Ann
Franchesca Laguna, Michael Niemier, Xiaobo Sharon Hu, Liang Zhao, Cheng Zhuo,
and Xunzhao Yin
|
COSIME: FeFET based Associative Memory for In-Memory Cosine Similarity
Search
|
Accepted by the 41st International Conference on Computer Aided
Design (ICCAD), San Diego, USA
| null | null | null |
cs.AR cs.ET
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a number of machine learning models, an input query is searched across the
trained class vectors to find the closest feature class vector in cosine
similarity metric. However, performing the cosine similarities between the
vectors in Von-Neumann machines involves a large number of multiplications,
Euclidean normalizations and division operations, thus incurring heavy hardware
energy and latency overheads. Moreover, due to the memory wall problem that
presents in the conventional architecture, frequent cosine similarity-based
searches (CSSs) over the class vectors requires a lot of data movements,
limiting the throughput and efficiency of the system. To overcome the
aforementioned challenges, this paper introduces COSIME, an general in-memory
associative memory (AM) engine based on the ferroelectric FET (FeFET) device
for efficient CSS. By leveraging the one-transistor AND gate function of FeFET
devices, current-based translinear analog circuit and winner-take-all (WTA)
circuitry, COSIME can realize parallel in-memory CSS across all the entries in
a memory block, and output the closest word to the input query in cosine
similarity metric. Evaluation results at the array level suggest that the
proposed COSIME design achieves 333X and 90.5X latency and energy improvements,
respectively, and realizes better classification accuracy when compared with an
AM design implementing approximated CSS. The proposed in-memory computing
fabric is evaluated for an HDC problem, showcasing that COSIME can achieve on
average 47.1X and 98.5X speedup and energy efficiency improvements compared
with an GPU implementation.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 13:24:40 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Liu",
"Che-Kai",
""
],
[
"Chen",
"Haobang",
""
],
[
"Imani",
"Mohsen",
""
],
[
"Ni",
"Kai",
""
],
[
"Kazemi",
"Arman",
""
],
[
"Laguna",
"Ann Franchesca",
""
],
[
"Niemier",
"Michael",
""
],
[
"Hu",
"Xiaobo Sharon",
""
],
[
"Zhao",
"Liang",
""
],
[
"Zhuo",
"Cheng",
""
],
[
"Yin",
"Xunzhao",
""
]
] |
new_dataset
| 0.979137 |
2207.12200
|
Miguel Lu\'is
|
Pedro Rito, Ana Almeida, Andreia Figueiredo, Christian Gomes, Pedro
Teixeira, Rodrigo Rosmaninho, Rui Lopes, Duarte Dias, Gon\c{c}alo V\'itor,
Gon\c{c}alo Perna, Miguel Silva, Carlos Senna, Duarte Raposo, Miguel Lu\'is,
Susana Sargento, Arnaldo Oliveira, Nuno Borges de Carvalho
|
Aveiro Tech City Living Lab: A Communication, Sensing and Computing
Platform for City Environments
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
This article presents the deployment and experimentation architecture of the
Aveiro Tech City Living Lab (ATCLL) in Aveiro, Portugal. This platform
comprises a large number of Internet-of-Things devices with communication,
sensing and computing capabilities. The communication infrastructure, built on
fiber and Millimeter-wave (mmWave) links, integrates a communication network
with radio terminals (WiFi, ITS-G5, C-V2X, 5G and LoRa(WAN)), multiprotocol,
spread throughout 44 connected points of access in the city. Additionally,
public transportation has also been equipped with communication and sensing
units. All these points combine and interconnect a set of sensors, such as
mobility (Radars, Lidars, video cameras) and environmental sensors. Combining
edge computing and cloud management to deploy the services and manage the
platform, and a data platform to gather and process the data, the living lab
supports a wide range of services and applications: IoT, intelligent
transportation systems and assisted driving, environmental monitoring,
emergency and safety, among others. This article describes the architecture,
implementation and deployment to make the overall platform to work and
integrate researchers and citizens. Moreover, it showcases some examples of the
performance metrics achieved in the city infrastructure, the data that can be
collected, visualized and used to build services and applications to the
cities, and, finally, different use cases in the mobility and safety scenarios.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 13:42:09 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Rito",
"Pedro",
""
],
[
"Almeida",
"Ana",
""
],
[
"Figueiredo",
"Andreia",
""
],
[
"Gomes",
"Christian",
""
],
[
"Teixeira",
"Pedro",
""
],
[
"Rosmaninho",
"Rodrigo",
""
],
[
"Lopes",
"Rui",
""
],
[
"Dias",
"Duarte",
""
],
[
"Vítor",
"Gonçalo",
""
],
[
"Perna",
"Gonçalo",
""
],
[
"Silva",
"Miguel",
""
],
[
"Senna",
"Carlos",
""
],
[
"Raposo",
"Duarte",
""
],
[
"Luís",
"Miguel",
""
],
[
"Sargento",
"Susana",
""
],
[
"Oliveira",
"Arnaldo",
""
],
[
"de Carvalho",
"Nuno Borges",
""
]
] |
new_dataset
| 0.999716 |
2207.12254
|
Alireza Ramezani
|
Adarsh Salagame, Shoghair Manjikian, Chenghao Wang, Kaushik Venkatesh
Krishnamurthy, Shreyansh Pitroda, Bibek Gupta, Tobias Jacob, Benjamin Mottis,
Eric Sihite, Milad Ramezani, Alireza Ramezani
|
A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal
Platform
|
arXiv admin note: text overlap with arXiv:2104.05834,
arXiv:2205.06392
| null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Animals, such as birds, widely use multi-modal locomotion by combining legged
and aerial mobility with dominant inertial effects. The robotic biomimicry of
this multi-modal locomotion feat can yield ultra-flexible systems in terms of
their ability to negotiate their task spaces. The main objective of this paper
is to discuss the challenges in achieving multi-modal locomotion, and to report
our progress in developing our quadrupedal robot capable of multi-modal
locomotion (legged and aerial locomotion), the Husky Carbon. We report the
mechanical and electrical components utilized in our robot, in addition to the
simulation and experimentation done to achieve our goal in developing a
versatile multi-modal robotic platform.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 15:18:21 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Salagame",
"Adarsh",
""
],
[
"Manjikian",
"Shoghair",
""
],
[
"Wang",
"Chenghao",
""
],
[
"Krishnamurthy",
"Kaushik Venkatesh",
""
],
[
"Pitroda",
"Shreyansh",
""
],
[
"Gupta",
"Bibek",
""
],
[
"Jacob",
"Tobias",
""
],
[
"Mottis",
"Benjamin",
""
],
[
"Sihite",
"Eric",
""
],
[
"Ramezani",
"Milad",
""
],
[
"Ramezani",
"Alireza",
""
]
] |
new_dataset
| 0.99715 |
2207.12267
|
Su-Kyoung Kim
|
Su Kyoung Kim, Michael Maurus, Mathias Trampler, Marc Tabie, Elsa
Andrea Kirchner
|
Continuous ErrP detections during multimodal human-robot interaction
| null | null | null | null |
cs.RO cs.HC cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Human-in-the-loop approaches are of great importance for robot applications.
In the presented study, we implemented a multimodal human-robot interaction
(HRI) scenario, in which a simulated robot communicates with its human partner
through speech and gestures. The robot announces its intention verbally and
selects the appropriate action using pointing gestures. The human partner, in
turn, evaluates whether the robot's verbal announcement (intention) matches the
action (pointing gesture) chosen by the robot. For cases where the verbal
announcement of the robot does not match the corresponding action choice of the
robot, we expect error-related potentials (ErrPs) in the human
electroencephalogram (EEG). These intrinsic evaluations of robot actions by
humans, evident in the EEG, were recorded in real time, continuously segmented
online and classified asynchronously. For feature selection, we propose an
approach that allows the combinations of forward and backward sliding windows
to train a classifier. We achieved an average classification performance of 91%
across 9 subjects. As expected, we also observed a relatively high variability
between the subjects. In the future, the proposed feature selection approach
will be extended to allow for customization of feature selection. To this end,
the best combinations of forward and backward sliding windows will be
automatically selected to account for inter-subject variability in
classification performance. In addition, we plan to use the intrinsic human
error evaluation evident in the error case by the ErrP in interactive
reinforcement learning to improve multimodal human-robot interaction.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 15:39:32 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Kim",
"Su Kyoung",
""
],
[
"Maurus",
"Michael",
""
],
[
"Trampler",
"Mathias",
""
],
[
"Tabie",
"Marc",
""
],
[
"Kirchner",
"Elsa Andrea",
""
]
] |
new_dataset
| 0.972914 |
2207.12310
|
Christian Mejia-Escobar
|
Javier Caicedo and Pamela Acosta and Romel Pozo and Henry Guilcapi and
Christian Mejia-Escobar
|
Estimaci\'on de \'areas de cultivo mediante Deep Learning y
programaci\'on convencional
|
21 pages, in Spanish, 17 figures, 3 tables
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Artificial Intelligence has enabled the implementation of more accurate and
efficient solutions to problems in various areas. In the agricultural sector,
one of the main needs is to know at all times the extent of land occupied or
not by crops in order to improve production and profitability. The traditional
methods of calculation demand the collection of data manually and in person in
the field, causing high labor costs, execution times, and inaccuracy in the
results. The present work proposes a new method based on Deep Learning
techniques complemented with conventional programming for the determination of
the area of populated and unpopulated crop areas. We have considered as a case
study one of the most recognized companies in the planting and harvesting of
sugar cane in Ecuador. The strategy combines a Generative Adversarial Neural
Network (GAN) that is trained on a dataset of aerial photographs of natural and
urban landscapes to improve image resolution; a Convolutional Neural Network
(CNN) trained on a dataset of aerial photographs of sugar cane plots to
distinguish populated or unpopulated crop areas; and a standard image
processing module for the calculation of areas in a percentage manner. The
experiments performed demonstrate a significant improvement in the quality of
the aerial photographs as well as a remarkable differentiation between
populated and unpopulated crop areas, consequently, a more accurate result of
cultivated and uncultivated areas. The proposed method can be extended to the
detection of possible pests, areas of weed vegetation, dynamic crop
development, and both qualitative and quantitative quality control.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 16:22:55 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Caicedo",
"Javier",
""
],
[
"Acosta",
"Pamela",
""
],
[
"Pozo",
"Romel",
""
],
[
"Guilcapi",
"Henry",
""
],
[
"Mejia-Escobar",
"Christian",
""
]
] |
new_dataset
| 0.960424 |
2207.12317
|
Peng Yin
|
Ivan Cisneros, Peng Yin, Ji Zhang, Howie Choset and Sebastian Scherer
|
ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and
Localization
|
UAV Localization dataset paper
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present the ALTO dataset, a vision-focused dataset for the development and
benchmarking of Visual Place Recognition and Localization methods for Unmanned
Aerial Vehicles. The dataset is composed of two long (approximately 150km and
260km) trajectories flown by a helicopter over Ohio and Pennsylvania, and it
includes high precision GPS-INS ground truth location data, high precision
accelerometer readings, laser altimeter readings, and RGB downward facing
camera imagery. In addition, we provide reference imagery over the flight
paths, which makes this dataset suitable for VPR benchmarking and other tasks
common in Localization, such as image registration and visual odometry. To the
author's knowledge, this is the largest real-world aerial-vehicle dataset of
this kind. Our dataset is available at https://github.com/MetaSLAM/ALTO.
|
[
{
"version": "v1",
"created": "Tue, 19 Jul 2022 21:13:44 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Cisneros",
"Ivan",
""
],
[
"Yin",
"Peng",
""
],
[
"Zhang",
"Ji",
""
],
[
"Choset",
"Howie",
""
],
[
"Scherer",
"Sebastian",
""
]
] |
new_dataset
| 0.999821 |
2207.12326
|
Lorenzo Ceragioli
|
Lorenzo Ceragioli, Letterio Galletta, Pierpaolo Degano, Luca Vigan\`o
|
Automatic Fair Exchanges
| null | null | null | null |
cs.CR cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a decentralized environment, exchanging resources requires users to
bargain until an agreement is found. Moreover, human agreements involve a
combination of collaborative and selfish behavior and often induce circularity,
complicating the evaluation of exchange requests. We introduce MuAC, a policy
language that allows users to state in isolation under which conditions they
are open to grant their resources and what they require in return. In MuAC,
exchange requests are evaluated automatically with the guarantee that the only
exchanges that will take place are those that mutually satisfy users'
conditions. Moreover, MuAC can be used as an enforcement mechanism to prevent
users from cheating. As a proof of concept, we implement a blockchain smart
contract that allows users to exchange their non-fungible tokens.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 16:34:58 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Ceragioli",
"Lorenzo",
""
],
[
"Galletta",
"Letterio",
""
],
[
"Degano",
"Pierpaolo",
""
],
[
"Viganò",
"Luca",
""
]
] |
new_dataset
| 0.989607 |
2207.12381
|
Khiem Le
|
Khiem H. Le, Hieu H. Pham, Thao BT. Nguyen, Tu A. Nguyen, Tien N.
Thanh, Cuong D. Do
|
LightX3ECG: A Lightweight and eXplainable Deep Learning System for
3-lead Electrocardiogram Classification
|
Under review at Biomedical Signal Processing and Control
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Cardiovascular diseases (CVDs) are a group of heart and blood vessel
disorders that is one of the most serious dangers to human health, and the
number of such patients is still growing. Early and accurate detection plays a
key role in successful treatment and intervention. Electrocardiogram (ECG) is
the gold standard for identifying a variety of cardiovascular abnormalities. In
clinical practices and most of the current research, standard 12-lead ECG is
mainly used. However, using a lower number of leads can make ECG more prevalent
as it can be conveniently recorded by portable or wearable devices. In this
research, we develop a novel deep learning system to accurately identify
multiple cardiovascular abnormalities by using only three ECG leads.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 17:49:29 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Le",
"Khiem H.",
""
],
[
"Pham",
"Hieu H.",
""
],
[
"Nguyen",
"Thao BT.",
""
],
[
"Nguyen",
"Tu A.",
""
],
[
"Thanh",
"Tien N.",
""
],
[
"Do",
"Cuong D.",
""
]
] |
new_dataset
| 0.998897 |
2207.12393
|
Hao Zhu
|
Hao Zhu, Wayne Wu, Wentao Zhu, Liming Jiang, Siwei Tang, Li Zhang,
Ziwei Liu, Chen Change Loy
|
CelebV-HQ: A Large-Scale Video Facial Attributes Dataset
|
ECCV 2022. Project Page: https://celebv-hq.github.io/ ; Dataset:
https://github.com/CelebV-HQ/CelebV-HQ
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Large-scale datasets have played indispensable roles in the recent success of
face generation/editing and significantly facilitated the advances of emerging
research fields. However, the academic community still lacks a video dataset
with diverse facial attribute annotations, which is crucial for the research on
face-related videos. In this work, we propose a large-scale, high-quality, and
diverse video dataset with rich facial attribute annotations, named the
High-Quality Celebrity Video Dataset (CelebV-HQ). CelebV-HQ contains 35,666
video clips with the resolution of 512x512 at least, involving 15,653
identities. All clips are labeled manually with 83 facial attributes, covering
appearance, action, and emotion. We conduct a comprehensive analysis in terms
of age, ethnicity, brightness stability, motion smoothness, head pose
diversity, and data quality to demonstrate the diversity and temporal coherence
of CelebV-HQ. Besides, its versatility and potential are validated on two
representative tasks, i.e., unconditional video generation and video facial
attribute editing. Furthermore, we envision the future potential of CelebV-HQ,
as well as the new opportunities and challenges it would bring to related
research directions. Data, code, and models are publicly available. Project
page: https://celebv-hq.github.io.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 17:57:07 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Zhu",
"Hao",
""
],
[
"Wu",
"Wayne",
""
],
[
"Zhu",
"Wentao",
""
],
[
"Jiang",
"Liming",
""
],
[
"Tang",
"Siwei",
""
],
[
"Zhang",
"Li",
""
],
[
"Liu",
"Ziwei",
""
],
[
"Loy",
"Chen Change",
""
]
] |
new_dataset
| 0.99986 |
2207.12394
|
Shengyu Huang
|
Shengyu Huang, Zan Gojcic, Jiahui Huang, Andreas Wieser, Konrad
Schindler
|
Dynamic 3D Scene Analysis by Point Cloud Accumulation
|
ECCV 2022, camera ready
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Multi-beam LiDAR sensors, as used on autonomous vehicles and mobile robots,
acquire sequences of 3D range scans ("frames"). Each frame covers the scene
sparsely, due to limited angular scanning resolution and occlusion. The
sparsity restricts the performance of downstream processes like semantic
segmentation or surface reconstruction. Luckily, when the sensor moves, frames
are captured from a sequence of different viewpoints. This provides
complementary information and, when accumulated in a common scene coordinate
frame, yields a denser sampling and a more complete coverage of the underlying
3D scene. However, often the scanned scenes contain moving objects. Points on
those objects are not correctly aligned by just undoing the scanner's
ego-motion. In the present paper, we explore multi-frame point cloud
accumulation as a mid-level representation of 3D scan sequences, and develop a
method that exploits inductive biases of outdoor street scenes, including their
geometric layout and object-level rigidity. Compared to state-of-the-art scene
flow estimators, our proposed approach aims to align all 3D points in a common
reference frame correctly accumulating the points on the individual objects.
Our approach greatly reduces the alignment errors on several benchmark
datasets. Moreover, the accumulated point clouds benefit high-level tasks like
surface reconstruction.
|
[
{
"version": "v1",
"created": "Mon, 25 Jul 2022 17:57:46 GMT"
}
] | 2022-07-26T00:00:00 |
[
[
"Huang",
"Shengyu",
""
],
[
"Gojcic",
"Zan",
""
],
[
"Huang",
"Jiahui",
""
],
[
"Wieser",
"Andreas",
""
],
[
"Schindler",
"Konrad",
""
]
] |
new_dataset
| 0.983069 |
2008.07898
|
Martin Kucera
|
Martin Ku\v{c}era, Ond\v{r}ej Such\'y
|
Minimum Eccentricity Shortest Path Problem with Respect to Structural
Parameters
| null | null |
10.1007/978-3-030-79987-8_31
| null |
cs.DS cs.CC
|
http://creativecommons.org/licenses/by/4.0/
|
The Minimum Eccentricity Shortest Path Problem consists in finding a shortest
path with minimum eccentricity in a given undirected graph. The problem is
known to be NP-complete and W[2]-hard with respect to the desired eccentricity.
We present fpt-algorithms for the problem parameterized by the modular width,
distance to cluster graph, the combination of treewidth with the desired
eccentricity, and maximum leaf number.
|
[
{
"version": "v1",
"created": "Tue, 18 Aug 2020 12:56:02 GMT"
},
{
"version": "v2",
"created": "Sun, 27 Jun 2021 19:58:26 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Jul 2022 18:53:29 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Kučera",
"Martin",
""
],
[
"Suchý",
"Ondřej",
""
]
] |
new_dataset
| 0.989907 |
2112.00584
|
Kai Wang
|
Kai Wang, Paul Guerrero, Vladimir Kim, Siddhartha Chaudhuri, Minhyuk
Sung, Daniel Ritchie
|
The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D
Shapes from Parts
|
European Conference on Computer Vision (ECCV) 2022
| null | null | null |
cs.GR cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present the Shape Part Slot Machine, a new method for assembling novel 3D
shapes from existing parts by performing contact-based reasoning. Our method
represents each shape as a graph of ``slots,'' where each slot is a region of
contact between two shape parts. Based on this representation, we design a
graph-neural-network-based model for generating new slot graphs and retrieving
compatible parts, as well as a gradient-descent-based optimization scheme for
assembling the retrieved parts into a complete shape that respects the
generated slot graph. This approach does not require any semantic part labels;
interestingly, it also does not require complete part geometries -- reasoning
about the slots proves sufficient to generate novel, high-quality 3D shapes. We
demonstrate that our method generates shapes that outperform existing
modeling-by-assembly approaches regarding quality, diversity, and structural
complexity.
|
[
{
"version": "v1",
"created": "Wed, 1 Dec 2021 15:54:54 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 22:56:38 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Wang",
"Kai",
""
],
[
"Guerrero",
"Paul",
""
],
[
"Kim",
"Vladimir",
""
],
[
"Chaudhuri",
"Siddhartha",
""
],
[
"Sung",
"Minhyuk",
""
],
[
"Ritchie",
"Daniel",
""
]
] |
new_dataset
| 0.999626 |
2112.01551
|
Dave Zhenyu Chen
|
Dave Zhenyu Chen, Qirui Wu, Matthias Nie{\ss}ner, Angel X. Chang
|
D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning
and Visual Grounding
|
Project website: https://daveredrum.github.io/D3Net/
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent studies on dense captioning and visual grounding in 3D have achieved
impressive results. Despite developments in both areas, the limited amount of
available 3D vision-language data causes overfitting issues for 3D visual
grounding and 3D dense captioning methods. Also, how to discriminatively
describe objects in complex 3D environments is not fully studied yet. To
address these challenges, we present D3Net, an end-to-end neural
speaker-listener architecture that can detect, describe and discriminate. Our
D3Net unifies dense captioning and visual grounding in 3D in a self-critical
manner. This self-critical property of D3Net also introduces discriminability
during object caption generation and enables semi-supervised training on
ScanNet data with partially annotated descriptions. Our method outperforms SOTA
methods in both tasks on the ScanRefer dataset, surpassing the SOTA 3D dense
captioning method by a significant margin.
|
[
{
"version": "v1",
"created": "Thu, 2 Dec 2021 19:00:06 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 11:49:32 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Chen",
"Dave Zhenyu",
""
],
[
"Wu",
"Qirui",
""
],
[
"Nießner",
"Matthias",
""
],
[
"Chang",
"Angel X.",
""
]
] |
new_dataset
| 0.998746 |
2112.02308
|
Hao Zhu
|
Yiyu Zhuang, Hao Zhu, Xusen Sun, Xun Cao
|
MoFaNeRF: Morphable Facial Neural Radiance Field
|
accepted to ECCV2022; code available at
http://github.com/zhuhao-nju/mofanerf
| null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a parametric model that maps free-view images into a vector space
of coded facial shape, expression and appearance with a neural radiance field,
namely Morphable Facial NeRF. Specifically, MoFaNeRF takes the coded facial
shape, expression and appearance along with space coordinate and view direction
as input to an MLP, and outputs the radiance of the space point for
photo-realistic image synthesis. Compared with conventional 3D morphable models
(3DMM), MoFaNeRF shows superiority in directly synthesizing photo-realistic
facial details even for eyes, mouths, and beards. Also, continuous face
morphing can be easily achieved by interpolating the input shape, expression
and appearance codes. By introducing identity-specific modulation and texture
encoder, our model synthesizes accurate photometric details and shows strong
representation ability. Our model shows strong ability on multiple applications
including image-based fitting, random generation, face rigging, face editing,
and novel view synthesis. Experiments show that our method achieves higher
representation ability than previous parametric models, and achieves
competitive performance in several applications. To the best of our knowledge,
our work is the first facial parametric model built upon a neural radiance
field that can be used in fitting, generation and manipulation. The code and
data is available at https://github.com/zhuhao-nju/mofanerf.
|
[
{
"version": "v1",
"created": "Sat, 4 Dec 2021 11:25:28 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 17:16:26 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Zhuang",
"Yiyu",
""
],
[
"Zhu",
"Hao",
""
],
[
"Sun",
"Xusen",
""
],
[
"Cao",
"Xun",
""
]
] |
new_dataset
| 0.979382 |
2112.02990
|
Yujin Chen
|
Yujin Chen, Matthias Nie{\ss}ner, Angela Dai
|
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D
Scene Understanding
|
Accepted by ECCV 2022, Video: https://youtu.be/qhGhWZmJq3U
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a new approach to instill 4D dynamic object priors into learned 3D
representations by unsupervised pre-training. We observe that dynamic movement
of an object through an environment provides important cues about its
objectness, and thus propose to imbue learned 3D representations with such
dynamic understanding, that can then be effectively transferred to improved
performance in downstream 3D semantic scene understanding tasks. We propose a
new data augmentation scheme leveraging synthetic 3D shapes moving in static 3D
environments, and employ contrastive learning under 3D-4D constraints that
encode 4D invariances into the learned 3D representations. Experiments
demonstrate that our unsupervised representation learning results in
improvement in downstream 3D semantic segmentation, object detection, and
instance segmentation tasks, and moreover, notably improves performance in
data-scarce scenarios.
|
[
{
"version": "v1",
"created": "Mon, 6 Dec 2021 13:09:07 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 11:54:27 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Chen",
"Yujin",
""
],
[
"Nießner",
"Matthias",
""
],
[
"Dai",
"Angela",
""
]
] |
new_dataset
| 0.997134 |
2112.06346
|
Liang Qiu
|
Liang Qiu, Yizhou Zhao, Jinchao Li, Pan Lu, Baolin Peng, Jianfeng Gao,
Song-Chun Zhu
|
ValueNet: A New Dataset for Human Value Driven Dialogue System
|
Paper accepted by AAAI 2022
| null |
10.1609/aaai.v36i10.21368
| null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Building a socially intelligent agent involves many challenges, one of which
is to teach the agent to speak guided by its value like a human. However,
value-driven chatbots are still understudied in the area of dialogue systems.
Most existing datasets focus on commonsense reasoning or social norm modeling.
In this work, we present a new large-scale human value dataset called ValueNet,
which contains human attitudes on 21,374 text scenarios. The dataset is
organized in ten dimensions that conform to the basic human value theory in
intercultural research. We further develop a Transformer-based value regression
model on ValueNet to learn the utility distribution. Comprehensive empirical
results show that the learned value model could benefit a wide range of
dialogue tasks. For example, by teaching a generative agent with reinforcement
learning and the rewards from the value model, our method attains
state-of-the-art performance on the personalized dialog generation dataset:
Persona-Chat. With values as additional features, existing emotion recognition
models enable capturing rich human emotions in the context, which further
improves the empathetic response generation performance in the
EmpatheticDialogues dataset. To the best of our knowledge, ValueNet is the
first large-scale text dataset for human value modeling, and we are the first
one trying to incorporate a value model into emotionally intelligent dialogue
systems. The dataset is available at https://liang-qiu.github.io/ValueNet/.
|
[
{
"version": "v1",
"created": "Sun, 12 Dec 2021 23:02:52 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Qiu",
"Liang",
""
],
[
"Zhao",
"Yizhou",
""
],
[
"Li",
"Jinchao",
""
],
[
"Lu",
"Pan",
""
],
[
"Peng",
"Baolin",
""
],
[
"Gao",
"Jianfeng",
""
],
[
"Zhu",
"Song-Chun",
""
]
] |
new_dataset
| 0.999102 |
2202.11781
|
Moinak Bhattacharya
|
Moinak Bhattacharya, Shubham Jain, Prateek Prasanna
|
RadioTransformer: A Cascaded Global-Focal Transformer for Visual
Attention-guided Disease Classification
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this work, we present RadioTransformer, a novel visual attention-driven
transformer framework, that leverages radiologists' gaze patterns and models
their visuo-cognitive behavior for disease diagnosis on chest radiographs.
Domain experts, such as radiologists, rely on visual information for medical
image interpretation. On the other hand, deep neural networks have demonstrated
significant promise in similar tasks even where visual interpretation is
challenging. Eye-gaze tracking has been used to capture the viewing behavior of
domain experts, lending insights into the complexity of visual search. However,
deep learning frameworks, even those that rely on attention mechanisms, do not
leverage this rich domain information. RadioTransformer fills this critical gap
by learning from radiologists' visual search patterns, encoded as 'human visual
attention regions' in a cascaded global-focal transformer framework. The
overall 'global' image characteristics and the more detailed 'local' features
are captured by the proposed global and focal modules, respectively. We
experimentally validate the efficacy of our student-teacher approach for 8
datasets involving different disease classification tasks where eye-gaze data
is not available during the inference phase. Code:
https://github.com/bmi-imaginelab/radiotransformer.
|
[
{
"version": "v1",
"created": "Wed, 23 Feb 2022 20:52:30 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 20:36:16 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Bhattacharya",
"Moinak",
""
],
[
"Jain",
"Shubham",
""
],
[
"Prasanna",
"Prateek",
""
]
] |
new_dataset
| 0.975187 |
2203.00795
|
Gedaliah Knizhnik
|
Gedaliah Knizhnik and Mark Yim
|
Amplitude Control for Parallel Lattices of Docked Modboats
|
7 pages. Accepted to the 2022 International Conference on Robotics
and Automation (ICRA)
| null |
10.1109/ICRA46639.2022.9812381
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Modboat is a low-cost, underactuated, modular robot capable of surface
swimming. It is able to swim individually, dock to other Modboats, and undock
from them using only a single motor and two passive flippers. Undocking without
additional actuation is achieved by causing intentional self-collision between
the tails of neighboring modules; this becomes a challenge when group swimming
as one connected component is desirable. In this work, we develop a control
strategy to allow parallel lattices of Modboats to swim as a single unit, which
conventionally requires holonomic modules. We show that the control strategy is
guaranteed to avoid unintentional undocking and minimizes internal forces
within the lattice. Experimental verification shows that the controller
performs well and is consistent for lattices of various sizes. Controllability
is maintained while swimming, but pure yaw control causes lateral movement that
cannot be counteracted by the presented framework.
|
[
{
"version": "v1",
"created": "Tue, 1 Mar 2022 23:48:53 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 01:01:25 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Knizhnik",
"Gedaliah",
""
],
[
"Yim",
"Mark",
""
]
] |
new_dataset
| 0.999272 |
2203.00796
|
Gedaliah Knizhnik
|
Gedaliah Knizhnik, Peihan Li, Xi Yu, and M. Ani Hsieh
|
Flow-Based Control of Marine Robots in Gyre-Like Environments
|
7 pages. Published at 2022 International Conference on Robotics and
Automation (ICRA)
| null |
10.1109/ICRA46639.2022.9812331
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a flow-based control strategy that enables resource-constrained
marine robots to patrol gyre-like flow environments on an orbital trajectory
with a periodicity in a given range. The controller does not require a detailed
model of the flow field and relies only on the robot's location relative to the
center of the gyre. Instead of precisely tracking a pre-defined trajectory, the
robots are tasked to stay in between two bounding trajectories with known
periodicity. Furthermore, the proposed strategy leverages the surrounding flow
field to minimize control effort. We prove that the proposed strategy enables
robots to cycle in the flow satisfying the desired periodicity requirements.
Our method is tested and validated both in simulation and in experiments using
a low-cost, underactuated, surface swimming robot, i.e. the Modboat.
|
[
{
"version": "v1",
"created": "Tue, 1 Mar 2022 23:53:29 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 00:59:51 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Knizhnik",
"Gedaliah",
""
],
[
"Li",
"Peihan",
""
],
[
"Yu",
"Xi",
""
],
[
"Hsieh",
"M. Ani",
""
]
] |
new_dataset
| 0.965104 |
2203.03041
|
Xuebin Qin
|
Xuebin Qin and Hang Dai and Xiaobin Hu and Deng-Ping Fan and Ling Shao
and Luc Van Gool
|
Highly Accurate Dichotomous Image Segmentation
|
29 pages, 18 figures, ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a systematic study on a new task called dichotomous image
segmentation (DIS) , which aims to segment highly accurate objects from natural
images. To this end, we collected the first large-scale DIS dataset, called
DIS5K, which contains 5,470 high-resolution (e.g., 2K, 4K or larger) images
covering camouflaged, salient, or meticulous objects in various backgrounds.
DIS is annotated with extremely fine-grained labels. Besides, we introduce a
simple intermediate supervision baseline (IS-Net) using both feature-level and
mask-level guidance for DIS model training. IS-Net outperforms various
cutting-edge baselines on the proposed DIS5K, making it a general self-learned
supervision network that can facilitate future research in DIS. Further, we
design a new metric called human correction efforts (HCE) which approximates
the number of mouse clicking operations required to correct the false positives
and false negatives. HCE is utilized to measure the gap between models and
real-world applications and thus can complement existing metrics. Finally, we
conduct the largest-scale benchmark, evaluating 16 representative segmentation
models, providing a more insightful discussion regarding object complexities,
and showing several potential applications (e.g., background removal, art
design, 3D reconstruction). Hoping these efforts can open up promising
directions for both academic and industries. Project page:
https://xuebinqin.github.io/dis/index.html.
|
[
{
"version": "v1",
"created": "Sun, 6 Mar 2022 20:09:19 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Mar 2022 19:13:10 GMT"
},
{
"version": "v3",
"created": "Tue, 12 Jul 2022 07:16:02 GMT"
},
{
"version": "v4",
"created": "Fri, 15 Jul 2022 14:28:49 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Qin",
"Xuebin",
""
],
[
"Dai",
"Hang",
""
],
[
"Hu",
"Xiaobin",
""
],
[
"Fan",
"Deng-Ping",
""
],
[
"Shao",
"Ling",
""
],
[
"Van Gool",
"Luc",
""
]
] |
new_dataset
| 0.993652 |
2204.02445
|
Xianghui Xie
|
Xianghui Xie, Bharat Lal Bhatnagar, Gerard Pons-Moll
|
CHORE: Contact, Human and Object REconstruction from a single RGB image
|
Accepted at ECCV 2022, Camera ready version
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Most prior works in perceiving 3D humans from images reason human in
isolation without their surroundings. However, humans are constantly
interacting with the surrounding objects, thus calling for models that can
reason about not only the human but also the object and their interaction. The
problem is extremely challenging due to heavy occlusions between humans and
objects, diverse interaction types and depth ambiguity. In this paper, we
introduce CHORE, a novel method that learns to jointly reconstruct the human
and the object from a single RGB image. CHORE takes inspiration from recent
advances in implicit surface learning and classical model-based fitting. We
compute a neural reconstruction of human and object represented implicitly with
two unsigned distance fields, a correspondence field to a parametric body and
an object pose field. This allows us to robustly fit a parametric body model
and a 3D object template, while reasoning about interactions. Furthermore,
prior pixel-aligned implicit learning methods use synthetic data and make
assumptions that are not met in the real data. We propose a elegant depth-aware
scaling that allows more efficient shape learning on real data. Experiments
show that our joint reconstruction learned with the proposed strategy
significantly outperforms the SOTA. Our code and models are available at
https://virtualhumans.mpi-inf.mpg.de/chore
|
[
{
"version": "v1",
"created": "Tue, 5 Apr 2022 18:38:06 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 16:14:33 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Xie",
"Xianghui",
""
],
[
"Bhatnagar",
"Bharat Lal",
""
],
[
"Pons-Moll",
"Gerard",
""
]
] |
new_dataset
| 0.993946 |
2204.04627
|
Fu-Jen Tsai
|
Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin, Chung-Chi Tsai, and Chia-Wen
Lin
|
Stripformer: Strip Transformer for Fast Image Deblurring
|
ECCV 2022 Oral Presentation
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Images taken in dynamic scenes may contain unwanted motion blur, which
significantly degrades visual quality. Such blur causes short- and long-range
region-specific smoothing artifacts that are often directional and non-uniform,
which is difficult to be removed. Inspired by the current success of
transformers on computer vision and image processing tasks, we develop,
Stripformer, a transformer-based architecture that constructs intra- and
inter-strip tokens to reweight image features in the horizontal and vertical
directions to catch blurred patterns with different orientations. It stacks
interlaced intra-strip and inter-strip attention layers to reveal blur
magnitudes. In addition to detecting region-specific blurred patterns of
various orientations and magnitudes, Stripformer is also a token-efficient and
parameter-efficient transformer model, demanding much less memory usage and
computation cost than the vanilla transformer but works better without relying
on tremendous training data. Experimental results show that Stripformer
performs favorably against state-of-the-art models in dynamic scene deblurring.
|
[
{
"version": "v1",
"created": "Sun, 10 Apr 2022 08:01:00 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 10:01:04 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Tsai",
"Fu-Jen",
""
],
[
"Peng",
"Yan-Tsung",
""
],
[
"Lin",
"Yen-Yu",
""
],
[
"Tsai",
"Chung-Chi",
""
],
[
"Lin",
"Chia-Wen",
""
]
] |
new_dataset
| 0.984036 |
2204.14109
|
Mathis Petrovich
|
Mathis Petrovich, Michael J. Black, G\"ul Varol
|
TEMOS: Generating diverse human motions from textual descriptions
|
ECCV 2022 Camera ready
| null | null | null |
cs.CV cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We address the problem of generating diverse 3D human motions from textual
descriptions. This challenging task requires joint modeling of both modalities:
understanding and extracting useful human-centric information from the text,
and then generating plausible and realistic sequences of human poses. In
contrast to most previous work which focuses on generating a single,
deterministic, motion from a textual description, we design a variational
approach that can produce multiple diverse human motions. We propose TEMOS, a
text-conditioned generative model leveraging variational autoencoder (VAE)
training with human motion data, in combination with a text encoder that
produces distribution parameters compatible with the VAE latent space. We show
the TEMOS framework can produce both skeleton-based animations as in prior
work, as well more expressive SMPL body motions. We evaluate our approach on
the KIT Motion-Language benchmark and, despite being relatively
straightforward, demonstrate significant improvements over the state of the
art. Code and models are available on our webpage.
|
[
{
"version": "v1",
"created": "Mon, 25 Apr 2022 14:53:06 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 09:07:31 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Petrovich",
"Mathis",
""
],
[
"Black",
"Michael J.",
""
],
[
"Varol",
"Gül",
""
]
] |
new_dataset
| 0.995562 |
2206.09024
|
Keyu Chen
|
Keyu Chen, Marzieh Babaeianjelodar, Yiwen Shi, Kamila Janmohamed,
Rupak Sarkar, Ingmar Weber, Thomas Davidson, Munmun De Choudhury, Jonathan
Huang, Shweta Yadav, Ashique Khudabukhsh, Preslav Ivanov Nakov, Chris Bauch,
Orestis Papakyriakopoulos, Kaveh Khoshnood, and Navin Kumar
|
Partisan US News Media Representations of Syrian Refugees
| null | null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
We investigate how representations of Syrian refugees (2011-2021) differ
across US partisan news outlets. We analyze 47,388 articles from the online US
media about Syrian refugees to detail differences in reporting between left-
and right-leaning media. We use various NLP techniques to understand these
differences. Our polarization and question answering results indicated that
left-leaning media tended to represent refugees as child victims, welcome in
the US, and right-leaning media cast refugees as Islamic terrorists. We noted
similar results with our sentiment and offensive speech scores over time, which
detail possibly unfavorable representations of refugees in right-leaning media.
A strength of our work is how the different techniques we have applied validate
each other. Based on our results, we provide several recommendations.
Stakeholders may utilize our findings to intervene around refugee
representations, and design communications campaigns that improve the way
society sees refugees and possibly aid refugee outcomes.
|
[
{
"version": "v1",
"created": "Fri, 17 Jun 2022 21:58:36 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Chen",
"Keyu",
""
],
[
"Babaeianjelodar",
"Marzieh",
""
],
[
"Shi",
"Yiwen",
""
],
[
"Janmohamed",
"Kamila",
""
],
[
"Sarkar",
"Rupak",
""
],
[
"Weber",
"Ingmar",
""
],
[
"Davidson",
"Thomas",
""
],
[
"De Choudhury",
"Munmun",
""
],
[
"Huang",
"Jonathan",
""
],
[
"Yadav",
"Shweta",
""
],
[
"Khudabukhsh",
"Ashique",
""
],
[
"Nakov",
"Preslav Ivanov",
""
],
[
"Bauch",
"Chris",
""
],
[
"Papakyriakopoulos",
"Orestis",
""
],
[
"Khoshnood",
"Kaveh",
""
],
[
"Kumar",
"Navin",
""
]
] |
new_dataset
| 0.998238 |
2206.10883
|
Zejiang Shen
|
Zejiang Shen, Kyle Lo, Lauren Yu, Nathan Dahlberg, Margo Schlanger,
Doug Downey
|
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple
Granularities
|
37 pages, 2 figures, 9 tables
| null | null | null |
cs.CL cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
With the advent of large language models, methods for abstractive
summarization have made great strides, creating potential for use in
applications to aid knowledge workers processing unwieldy document collections.
One such setting is the Civil Rights Litigation Clearinghouse (CRLC)
(https://clearinghouse.net),which posts information about large-scale civil
rights lawsuits, serving lawyers, scholars, and the general public. Today,
summarization in the CRLC requires extensive training of lawyers and law
students who spend hours per case understanding multiple relevant documents in
order to produce high-quality summaries of key events and outcomes. Motivated
by this ongoing real-world summarization effort, we introduce Multi-LexSum, a
collection of 9,280 expert-authored summaries drawn from ongoing CRLC writing.
Multi-LexSum presents a challenging multi-document summarization task given the
length of the source documents, often exceeding two hundred pages per case.
Furthermore, Multi-LexSum is distinct from other datasets in its multiple
target summaries, each at a different granularity (ranging from one-sentence
"extreme" summaries to multi-paragraph narrations of over five hundred words).
We present extensive analysis demonstrating that despite the high-quality
summaries in the training data (adhering to strict content and style
guidelines), state-of-the-art summarization models perform poorly on this task.
We release Multi-LexSum for further research in summarization methods as well
as to facilitate development of applications to assist in the CRLC's mission at
https://multilexsum.github.io.
|
[
{
"version": "v1",
"created": "Wed, 22 Jun 2022 07:26:55 GMT"
},
{
"version": "v2",
"created": "Thu, 23 Jun 2022 23:40:10 GMT"
},
{
"version": "v3",
"created": "Fri, 22 Jul 2022 17:37:58 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Shen",
"Zejiang",
""
],
[
"Lo",
"Kyle",
""
],
[
"Yu",
"Lauren",
""
],
[
"Dahlberg",
"Nathan",
""
],
[
"Schlanger",
"Margo",
""
],
[
"Downey",
"Doug",
""
]
] |
new_dataset
| 0.996997 |
2206.12931
|
Ritesh Kumar
|
Ritesh Kumar, Siddharth Singh, Shyam Ratan, Mohit Raj, Sonal Sinha,
Bornini Lahiri, Vivek Seshadri, Kalika Bali and Atul Kr. Ojha
|
Annotated Speech Corpus for Low Resource Indian Languages: Awadhi,
Bhojpuri, Braj and Magahi
|
Speech for Social Good Workshop, 2022, Interspeech 2022
| null | null | null |
cs.CL cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we discuss an in-progress work on the development of a speech
corpus for four low-resource Indo-Aryan languages -- Awadhi, Bhojpuri, Braj and
Magahi using the field methods of linguistic data collection. The total size of
the corpus currently stands at approximately 18 hours (approx. 4-5 hours each
language) and it is transcribed and annotated with grammatical information such
as part-of-speech tags, morphological features and Universal dependency
relationships. We discuss our methodology for data collection in these
languages, most of which was done in the middle of the COVID-19 pandemic, with
one of the aims being to generate some additional income for low-income groups
speaking these languages. In the paper, we also discuss the results of the
baseline experiments for automatic speech recognition system in these
languages.
|
[
{
"version": "v1",
"created": "Sun, 26 Jun 2022 17:28:38 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Kumar",
"Ritesh",
""
],
[
"Singh",
"Siddharth",
""
],
[
"Ratan",
"Shyam",
""
],
[
"Raj",
"Mohit",
""
],
[
"Sinha",
"Sonal",
""
],
[
"Lahiri",
"Bornini",
""
],
[
"Seshadri",
"Vivek",
""
],
[
"Bali",
"Kalika",
""
],
[
"Ojha",
"Atul Kr.",
""
]
] |
new_dataset
| 0.997885 |
2207.04789
|
Ilia Petrov
|
Bernhard M\"o{\ss}ner, Christian Riegger, Arthur Bernhardt, Ilia
Petrov
|
bloomRF: On Performing Range-Queries in Bloom-Filters with
Piecewise-Monotone Hash Functions and Prefix Hashing
|
Extended version. Original accepted at EDBT 2023
| null | null | null |
cs.DB
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We introduce bloomRF as a unified method for approximate membership testing
that supports both point- and range-queries. As a first core idea, bloomRF
introduces novel prefix hashing to efficiently encode range information in the
hash-code of the key itself. As a second key concept, bloomRF proposes novel
piecewise-monotone hash-functions that preserve local order and support fast
range-lookups with fewer memory accesses. bloomRF has near-optimal space
complexity and constant query complexity. Although, bloomRF is designed for
integer domains, it supports floating-points, and can serve as a
multi-attribute filter. The evaluation in RocksDB and in a standalone library
shows that it is more efficient and outperforms existing point-range-filters by
up to 4x across a range of settings and distributions, while keeping the
false-positive rate low.
|
[
{
"version": "v1",
"created": "Mon, 11 Jul 2022 11:42:25 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 12:54:45 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Mößner",
"Bernhard",
""
],
[
"Riegger",
"Christian",
""
],
[
"Bernhardt",
"Arthur",
""
],
[
"Petrov",
"Ilia",
""
]
] |
new_dataset
| 0.953172 |
2207.06067
|
Omid Nejati Manzari
|
Omid Nejati Manzari, Amin Boudesh, Shahriar B. Shokouhi
|
Pyramid Transformer for Traffic Sign Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Traffic sign detection is a vital task in the visual system of self-driving
cars and the automated driving system. Recently, novel Transformer-based models
have achieved encouraging results for various computer vision tasks. We still
observed that vanilla ViT could not yield satisfactory results in traffic sign
detection because the overall size of the datasets is very small and the class
distribution of traffic signs is extremely unbalanced. To overcome this
problem, a novel Pyramid Transformer with locality mechanisms is proposed in
this paper. Specifically, Pyramid Transformer has several spatial pyramid
reduction layers to shrink and embed the input image into tokens with rich
multi-scale context by using atrous convolutions. Moreover, it inherits an
intrinsic scale invariance inductive bias and is able to learn local feature
representation for objects at various scales, thereby enhancing the network
robustness against the size discrepancy of traffic signs. The experiments are
conducted on the German Traffic Sign Detection Benchmark (GTSDB). The results
demonstrate the superiority of the proposed model in the traffic sign detection
tasks. More specifically, Pyramid Transformer achieves 77.8% mAP on GTSDB when
applied to the Cascade RCNN as the backbone, which surpasses most well-known
and widely-used state-of-the-art models.
|
[
{
"version": "v1",
"created": "Wed, 13 Jul 2022 09:21:19 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 07:17:55 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Manzari",
"Omid Nejati",
""
],
[
"Boudesh",
"Amin",
""
],
[
"Shokouhi",
"Shahriar B.",
""
]
] |
new_dataset
| 0.988792 |
2207.06706
|
Ariel Caputo
|
Ariel Caputo, Marco Emporio, Andrea Giachetti, Marco Cristani, Guido
Borghi, Andrea D'Eusanio, Minh-Quan Le, Hai-Dang Nguyen, Minh-Triet Tran, F.
Ambellan, M. Hanik, E. Nava-Yazdani, C. von Tycowicz
|
SHREC 2022 Track on Online Detection of Heterogeneous Gestures
|
Accepted on Computer & Graphics journal
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents the outcomes of a contest organized to evaluate methods
for the online recognition of heterogeneous gestures from sequences of 3D hand
poses. The task is the detection of gestures belonging to a dictionary of 16
classes characterized by different pose and motion features. The dataset
features continuous sequences of hand tracking data where the gestures are
interleaved with non-significant motions. The data have been captured using the
Hololens 2 finger tracking system in a realistic use-case of mixed reality
interaction. The evaluation is based not only on the detection performances but
also on the latency and the false positives, making it possible to understand
the feasibility of practical interaction tools based on the algorithms
proposed. The outcomes of the contest's evaluation demonstrate the necessity of
further research to reduce recognition errors, while the computational cost of
the algorithms proposed is sufficiently low.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 07:24:02 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 11:51:49 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Caputo",
"Ariel",
""
],
[
"Emporio",
"Marco",
""
],
[
"Giachetti",
"Andrea",
""
],
[
"Cristani",
"Marco",
""
],
[
"Borghi",
"Guido",
""
],
[
"D'Eusanio",
"Andrea",
""
],
[
"Le",
"Minh-Quan",
""
],
[
"Nguyen",
"Hai-Dang",
""
],
[
"Tran",
"Minh-Triet",
""
],
[
"Ambellan",
"F.",
""
],
[
"Hanik",
"M.",
""
],
[
"Nava-Yazdani",
"E.",
""
],
[
"von Tycowicz",
"C.",
""
]
] |
new_dataset
| 0.995438 |
2207.09298
|
Houkun Zhu
|
Houkun Zhu, Dominik Scheinert, Lauritz Thamsen, Kordian Gontarska, and
Odej Kao
|
Magpie: Automatically Tuning Static Parameters for Distributed File
Systems using Deep Reinforcement Learning
|
Accepted at The IEEE International Conference on Cloud Engineering
(IC2E) conference 2022
| null | null | null |
cs.DC cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Distributed file systems are widely used nowadays, yet using their default
configurations is often not optimal. At the same time, tuning configuration
parameters is typically challenging and time-consuming. It demands expertise
and tuning operations can also be expensive. This is especially the case for
static parameters, where changes take effect only after a restart of the system
or workloads. We propose a novel approach, Magpie, which utilizes deep
reinforcement learning to tune static parameters by strategically exploring and
exploiting configuration parameter spaces. To boost the tuning of the static
parameters, our method employs both server and client metrics of distributed
file systems to understand the relationship between static parameters and
performance. Our empirical evaluation results show that Magpie can noticeably
improve the performance of the distributed file system Lustre, where our
approach on average achieves 91.8% throughput gains against default
configuration after tuning towards single performance indicator optimization,
while it reaches 39.7% more throughput gains against the baseline.
|
[
{
"version": "v1",
"created": "Tue, 19 Jul 2022 14:32:07 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 13:53:19 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Zhu",
"Houkun",
""
],
[
"Scheinert",
"Dominik",
""
],
[
"Thamsen",
"Lauritz",
""
],
[
"Gontarska",
"Kordian",
""
],
[
"Kao",
"Odej",
""
]
] |
new_dataset
| 0.979879 |
2207.10106
|
Tatsuya Matsushima
|
Tatsuya Matsushima, Yuki Noguchi, Jumpei Arima, Toshiki Aoki, Yuki
Okita, Yuya Ikeda, Koki Ishimoto, Shohei Taniguchi, Yuki Yamashita, Shoichi
Seto, Shixiang Shane Gu, Yusuke Iwasawa, Yutaka Matsuo
|
World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for
Room Tidying with Mobile Manipulator
| null | null | null | null |
cs.RO cs.AI cs.CV cs.LG cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Tidying up a household environment using a mobile manipulator poses various
challenges in robotics, such as adaptation to large real-world environmental
variations, and safe and robust deployment in the presence of humans.The
Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global
competition held in September 2021, benchmarked tidying tasks in the real home
environments, and importantly, tested for full system performances.For this
challenge, we developed an entire household service robot system, which
leverages a data-driven approach to adapt to numerous edge cases that occur
during the execution, instead of classical manual pre-programmed solutions. In
this paper, we describe the core ingredients of the proposed robot system,
including visual recognition, object manipulation, and motion planning. Our
robot system won the second prize, verifying the effectiveness and potential of
data-driven robot systems for mobile manipulation in home environments.
|
[
{
"version": "v1",
"created": "Wed, 20 Jul 2022 18:00:20 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 01:44:49 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Matsushima",
"Tatsuya",
""
],
[
"Noguchi",
"Yuki",
""
],
[
"Arima",
"Jumpei",
""
],
[
"Aoki",
"Toshiki",
""
],
[
"Okita",
"Yuki",
""
],
[
"Ikeda",
"Yuya",
""
],
[
"Ishimoto",
"Koki",
""
],
[
"Taniguchi",
"Shohei",
""
],
[
"Yamashita",
"Yuki",
""
],
[
"Seto",
"Shoichi",
""
],
[
"Gu",
"Shixiang Shane",
""
],
[
"Iwasawa",
"Yusuke",
""
],
[
"Matsuo",
"Yutaka",
""
]
] |
new_dataset
| 0.982247 |
2207.10120
|
Davide Moltisanti
|
Davide Moltisanti, Jinyi Wu, Bo Dai, Chen Change Loy
|
BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis
|
ECCV 2022. Dataset available at https://github.com/dmoltisanti/brace
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generative models for audio-conditioned dance motion synthesis map music
features to dance movements. Models are trained to associate motion patterns to
audio patterns, usually without an explicit knowledge of the human body. This
approach relies on a few assumptions: strong music-dance correlation,
controlled motion data and relatively simple poses and movements. These
characteristics are found in all existing datasets for dance motion synthesis,
and indeed recent methods can achieve good results.We introduce a new dataset
aiming to challenge these common assumptions, compiling a set of dynamic dance
sequences displaying complex human poses. We focus on breakdancing which
features acrobatic moves and tangled postures. We source our data from the Red
Bull BC One competition videos. Estimating human keypoints from these videos is
difficult due to the complexity of the dance, as well as the multiple moving
cameras recording setup. We adopt a hybrid labelling pipeline leveraging deep
estimation models as well as manual annotations to obtain good quality keypoint
sequences at a reduced cost. Our efforts produced the BRACE dataset, which
contains over 3 hours and 30 minutes of densely annotated poses. We test
state-of-the-art methods on BRACE, showing their limitations when evaluated on
complex sequences. Our dataset can readily foster advance in dance motion
synthesis. With intricate poses and swift movements, models are forced to go
beyond learning a mapping between modalities and reason more effectively about
body structure and movements.
|
[
{
"version": "v1",
"created": "Wed, 20 Jul 2022 18:03:54 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Jul 2022 13:02:35 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Moltisanti",
"Davide",
""
],
[
"Wu",
"Jinyi",
""
],
[
"Dai",
"Bo",
""
],
[
"Loy",
"Chen Change",
""
]
] |
new_dataset
| 0.999836 |
2207.10479
|
Lukas Daniel Klausner
|
Angelika Adensamer and Rita Gsenger and Lukas Daniel Klausner
|
Wer ist schuld, wenn Algorithmen irren? Entscheidungsautomatisierung,
Organisationen und Verantwortung
|
18 pages, 2 figures, in German
|
Publikation zur Wissenschaftskonferenz der Arbeiterkammer
Vorarlberg im September 2021 (Forschung 1: Technikfolgenabschaetzung aus
Arbeitnehmer:innenperspektive), 2022, 47-73
| null | null |
cs.CY cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Algorithmic decision support (ADS) is increasingly used in a whole array of
different contexts and structures in various areas of society, influencing many
people's lives. Its use raises questions, among others, about accountability,
transparency and responsibility. Our article aims to give a brief overview of
the central issues connected to ADS, responsibility and decision-making in
organisational contexts and identify open questions and research gaps.
Furthermore, we describe a set of guidelines and a complementary digital tool
to assist practitioners in mapping responsibility when introducing ADS within
their organisational context.
--
Algorithmenunterst\"utzte Entscheidungsfindung (algorithmic decision support,
ADS) kommt in verschiedenen Kontexten und Strukturen vermehrt zum Einsatz und
beeinflusst in diversen gesellschaftlichen Bereichen das Leben vieler Menschen.
Ihr Einsatz wirft einige Fragen auf, unter anderem zu den Themen Rechenschaft,
Transparenz und Verantwortung. Im Folgenden m\"ochten wir einen \"Uberblick
\"uber die wichtigsten Fragestellungen rund um ADS, Verantwortung und
Entscheidungsfindung in organisationalen Kontexten geben und einige offene
Fragen und Forschungsl\"ucken aufzeigen. Weiters beschreiben wir als konkrete
Hilfestellung f\"ur die Praxis einen von uns entwickelten Leitfaden samt
erg\"anzendem digitalem Tool, welches Anwender:innen insbesondere bei der
Verortung und Zuordnung von Verantwortung bei der Nutzung von ADS in
organisationalen Kontexten helfen soll.
|
[
{
"version": "v1",
"created": "Thu, 21 Jul 2022 13:45:10 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Adensamer",
"Angelika",
""
],
[
"Gsenger",
"Rita",
""
],
[
"Klausner",
"Lukas Daniel",
""
]
] |
new_dataset
| 0.998565 |
2207.10690
|
Yue Sun
|
Yue Sun, Honggang Zhang, Zhuoming Huang, and Benyuan Liu
|
R2P: A Deep Learning Model from mmWave Radar to Point Cloud
|
arXiv admin note: text overlap with arXiv:2109.09188
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Recent research has shown the effectiveness of mmWave radar sensing for
object detection in low visibility environments, which makes it an ideal
technique in autonomous navigation systems. In this paper, we introduce Radar
to Point Cloud (R2P), a deep learning model that generates smooth, dense, and
highly accurate point cloud representation of a 3D object with fine geometry
details, based on rough and sparse point clouds with incorrect points obtained
from mmWave radar. These input point clouds are converted from the 2D depth
images that are generated from raw mmWave radar sensor data, characterized by
inconsistency, and orientation and shape errors. R2P utilizes an architecture
of two sequential deep learning encoder-decoder blocks to extract the essential
features of those radar-based input point clouds of an object when observed
from multiple viewpoints, and to ensure the internal consistency of a generated
output point cloud and its accurate and detailed shape reconstruction of the
original object. We implement R2P to replace Stage 2 of our recently proposed
3DRIMR (3D Reconstruction and Imaging via mmWave Radar) system. Our experiments
demonstrate the significant performance improvement of R2P over the popular
existing methods such as PointNet, PCN, and the original 3DRIMR design.
|
[
{
"version": "v1",
"created": "Thu, 21 Jul 2022 18:01:05 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Sun",
"Yue",
""
],
[
"Zhang",
"Honggang",
""
],
[
"Huang",
"Zhuoming",
""
],
[
"Liu",
"Benyuan",
""
]
] |
new_dataset
| 0.997172 |
2207.10693
|
Anton Bredenbeck
|
Anton Bredenbeck, Shubham Vyas, Martin Zwick, Dorit Borrmann, Miguel
Olivares-Mendez, Andreas N\"uchter
|
Trajectory Optimization and Following for a Three Degrees of Freedom
Overactuated Floating Platform
|
Accepted to IROS2022, code at
https://gitlab.com/anton.bredenbeck/ff-trajectories
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Space robotics applications, such as Active Space Debris Removal (ASDR),
require representative testing before launch. A commonly used approach to
emulate the microgravity environment in space is air-bearing based platforms on
flat-floors, such as the European Space Agency's Orbital Robotics and GNC Lab
(ORGL). This work proposes a control architecture for a floating platform at
the ORGL, equipped with eight solenoid-valve-based thrusters and one reaction
wheel. The control architecture consists of two main components: a trajectory
planner that finds optimal trajectories connecting two states and a trajectory
follower that follows any physically feasible trajectory. The controller is
first evaluated within an introduced simulation, achieving a 100 % success rate
at finding and following trajectories to the origin within a Monte-Carlo test.
Individual trajectories are also successfully followed by the physical system.
In this work, we showcase the ability of the controller to reject disturbances
and follow a straight-line trajectory within tens of centimeters.
|
[
{
"version": "v1",
"created": "Thu, 21 Jul 2022 18:06:20 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Bredenbeck",
"Anton",
""
],
[
"Vyas",
"Shubham",
""
],
[
"Zwick",
"Martin",
""
],
[
"Borrmann",
"Dorit",
""
],
[
"Olivares-Mendez",
"Miguel",
""
],
[
"Nüchter",
"Andreas",
""
]
] |
new_dataset
| 0.970499 |
2207.10761
|
Gabriel Sarch
|
Gabriel Sarch, Zhaoyuan Fang, Adam W. Harley, Paul Schydlo, Michael J.
Tarr, Saurabh Gupta, and Katerina Fragkiadaki
|
TIDEE: Tidying Up Novel Rooms using Visuo-Semantic Commonsense Priors
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce TIDEE, an embodied agent that tidies up a disordered scene based
on learned commonsense object placement and room arrangement priors. TIDEE
explores a home environment, detects objects that are out of their natural
place, infers plausible object contexts for them, localizes such contexts in
the current scene, and repositions the objects. Commonsense priors are encoded
in three modules: i) visuo-semantic detectors that detect out-of-place objects,
ii) an associative neural graph memory of objects and spatial relations that
proposes plausible semantic receptacles and surfaces for object repositions,
and iii) a visual search network that guides the agent's exploration for
efficiently localizing the receptacle-of-interest in the current scene to
reposition the object. We test TIDEE on tidying up disorganized scenes in the
AI2THOR simulation environment. TIDEE carries out the task directly from pixel
and raw depth input without ever having observed the same room beforehand,
relying only on priors learned from a separate set of training houses. Human
evaluations on the resulting room reorganizations show TIDEE outperforms
ablative versions of the model that do not use one or more of the commonsense
priors. On a related room rearrangement benchmark that allows the agent to view
the goal state prior to rearrangement, a simplified version of our model
significantly outperforms a top-performing method by a large margin. Code and
data are available at the project website: https://tidee-agent.github.io/.
|
[
{
"version": "v1",
"created": "Thu, 21 Jul 2022 21:19:18 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Sarch",
"Gabriel",
""
],
[
"Fang",
"Zhaoyuan",
""
],
[
"Harley",
"Adam W.",
""
],
[
"Schydlo",
"Paul",
""
],
[
"Tarr",
"Michael J.",
""
],
[
"Gupta",
"Saurabh",
""
],
[
"Fragkiadaki",
"Katerina",
""
]
] |
new_dataset
| 0.992732 |
2207.10789
|
Omer Aydin
|
Omer Aydin
|
Authentication and Billing Scheme for The Electric Vehicles: EVABS
| null | null |
10.33461/uybisbbd.1075481
| null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The need for different energy sources has increased due to the decrease in
the amount and the harm caused to the environment by its usage. Today, fossil
fuels used as an energy source in land, sea or air vehicles are rapidly being
replaced by different energy sources. The number and types of vehicles using
energy sources other than fossil fuels are also increasing. Electricity stands
out among the energy sources used. The possibility of generating electricity
that is renewable, compatible with nature and at a lower cost provides a great
advantage. For all these reasons, the use of electric vehicles is increasing
day by day. Various solutions continue to be developed for the charging systems
and post-charge billing processes of these vehicles. As a result of these
solutions, the standards have not yet been fully formed. In this study, an
authentication and billing scheme is proposed for charging and post-charging
billing processes of electric land vehicles keeping security and privacy in the
foreground. This scheme is named EVABS, which derives from the phrase "Electric
Vehicle Authentication and Billing Scheme". An authentication and billing
scheme is proposed where data communication is encrypted, payment transactions
are handled securely and parties can authenticate over wired or wireless. The
security of the proposed scheme has been examined theoretically and it has been
determined that it is secure against known attacks.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 23:29:24 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Aydin",
"Omer",
""
]
] |
new_dataset
| 0.959607 |
2207.10795
|
Conner Bender
|
Conner Bender
|
DJI drone IDs are not encrypted
|
13 pages, 15 figures, 5 tables, 10 algorithms
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Drones are widely used in the energy, construction, agriculture,
transportation, warehousing, real estate and movie industries. Key applications
include surveys, inspections, deliveries and cinematography. With approximately
70-80% of the global market share of commercial off-the-shelf drones, Da-Jiang
Innovations (DJI), headquartered in Shenzhen, China, essentially monopolizes
the drone market. As commercial-off-the-shelf drone sales steadily rise, the
Federal Aviation Administration has instituted regulations to protect the
federal airspace. DJI has become a pioneer in developing remote identification
technology in the form of drone ID (also known as AeroScope signals). Despite
claims from the company touting its implementation of drone ID technology as
"encrypted" yet later being proved incorrect for the claim, it remains a
mystery on how one can grab and decode drone IDs over the air with low-cost
radio frequency hardware in real-time. This research paper discusses a
methodology using radio software and hardware to detect both Enhanced Wi-Fi and
OcuSync drone IDs, the three types of drone ID packet structures and a
functioning prototype of a DJI OcuSync detection system equipped with two
HackRF Ones.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 18:15:27 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Bender",
"Conner",
""
]
] |
new_dataset
| 0.987524 |
2207.10806
|
Andrew Critch PhD
|
Andrew Critch
|
WordSig: QR streams enabling platform-independent self-identification
that's impossible to deepfake
| null | null | null | null |
cs.CR cs.AI cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Deepfakes can degrade the fabric of society by limiting our ability to trust
video content from leaders, authorities, and even friends. Cryptographically
secure digital signatures may be used by video streaming platforms to endorse
content, but these signatures are applied by the content distributor rather
than the participants in the video. We introduce WordSig, a simple protocol
allowing video participants to digitally sign the words they speak using a
stream of QR codes, and allowing viewers to verify the consistency of
signatures across videos. This allows establishing a trusted connection between
the viewer and the participant that is not mediated by the content distributor.
Given the widespread adoption of QR codes for distributing hyperlinks and
vaccination records, and the increasing prevalence of celebrity deepfakes, 2022
or later may be a good time for public figures to begin using and promoting
QR-based self-authentication tools.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 17:23:01 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Critch",
"Andrew",
""
]
] |
new_dataset
| 0.991907 |
2207.10810
|
Hamed Farkhari
|
Joseanne Viana, Hamed Farkhari, Luis Miguel Campos, Pedro Sebastiao,
Katerina Koutlia, Sandra Lagen, Luis Bernardo, Rui Dinis
|
A Convolutional Attention Based Deep Network Solution for UAV Network
Attack Recognition over Fading Channels and Interference
|
6 pages, 6 figures
| null | null | null |
cs.CR cs.LG cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When users exchange data with Unmanned Aerial vehicles - (UAVs) over
air-to-ground (A2G) wireless communication networks, they expose the link to
attacks that could increase packet loss and might disrupt connectivity. For
example, in emergency deliveries, losing control information (i.e data related
to the UAV control communication) might result in accidents that cause UAV
destruction and damage to buildings or other elements in a city. To prevent
these problems, these issues must be addressed in 5G and 6G scenarios. This
research offers a deep learning (DL) approach for detecting attacks in UAVs
equipped with orthogonal frequency division multiplexing (OFDM) receivers on
Clustered Delay Line (CDL) channels in highly complex scenarios involving
authenticated terrestrial users, as well as attackers in unknown locations. We
use the two observable parameters available in 5G UAV connections: the Received
Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise
Ratio (SINR). The prospective algorithm is generalizable regarding attack
identification, which does not occur during training. Further, it can identify
all the attackers in the environment with 20 terrestrial users. A deeper
investigation into the timing requirements for recognizing attacks show that
after training, the minimum time necessary after the attack begins is 100 ms,
and the minimum attack power is 2 dBm, which is the same power that the
authenticated UAV uses. Our algorithm also detects moving attackers from a
distance of 500 m.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 22:08:12 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Viana",
"Joseanne",
""
],
[
"Farkhari",
"Hamed",
""
],
[
"Campos",
"Luis Miguel",
""
],
[
"Sebastiao",
"Pedro",
""
],
[
"Koutlia",
"Katerina",
""
],
[
"Lagen",
"Sandra",
""
],
[
"Bernardo",
"Luis",
""
],
[
"Dinis",
"Rui",
""
]
] |
new_dataset
| 0.992864 |
2207.10812
|
Ammar Haydari
|
Ammar Haydari, Yasin Yilmaz
|
RSU-Based Online Intrusion Detection and Mitigation for VANET
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Secure vehicular communication is a critical factor for secure traffic
management. Effective security in intelligent transportation systems (ITS)
requires effective and timely intrusion detection systems (IDS). In this paper,
we consider false data injection attacks and distributed denial-of-service
(DDoS) attacks, especially the stealthy DDoS attacks, targeting the integrity
and availability, respectively, in vehicular ad-hoc networks (VANET). Novel
statistical intrusion detection and mitigation techniques based on centralized
communications through roadside units (RSU) are proposed for the considered
attacks. The performance of the proposed methods are evaluated using a traffic
simulator and a real traffic dataset. Comparisons with the state-of-the-art
solutions clearly demonstrate the superior performance of the proposed methods
in terms of quick and accurate detection and localization of cyberattacks.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 19:26:46 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Haydari",
"Ammar",
""
],
[
"Yilmaz",
"Yasin",
""
]
] |
new_dataset
| 0.972064 |
2207.10817
|
Shakeel Ahmad Sheikh
|
Shakeel Ahmad Sheikh, Md Sahidullah, Fabrice Hirsch, Slim Ouni
|
End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering
Sub-Challenge
|
Accepted in ACM MM 2022 Conference : Grand Challenges, "\c{opyright}
{Owner/Author | ACM} {2022}. This is the author's version of the work. It is
posted here for your personal use. Not for redistribution
| null | null | null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/publicdomain/zero/1.0/
|
In this paper, we present end-to-end and speech embedding based systems
trained in a self-supervised fashion to participate in the ACM Multimedia 2022
ComParE Challenge, specifically the stuttering sub-challenge. In particular, we
exploit the embeddings from the pre-trained Wav2Vec2.0 model for stuttering
detection (SD) on the KSoF dataset. After embedding extraction, we benchmark
with several methods for SD. Our proposed self-supervised based SD system
achieves a UAR of 36.9% and 41.0% on validation and test sets respectively,
which is 31.32% (validation set) and 1.49% (test set) higher than the best
(DeepSpectrum) challenge baseline (CBL). Moreover, we show that concatenating
layer embeddings with Mel-frequency cepstral coefficients (MFCCs) features
further improves the UAR of 33.81% and 5.45% on validation and test sets
respectively over the CBL. Finally, we demonstrate that the summing information
across all the layers of Wav2Vec2.0 surpasses the CBL by a relative margin of
45.91% and 5.69% on validation and test sets respectively. Grand-challenge:
Computational Paralinguistics ChallengE
|
[
{
"version": "v1",
"created": "Wed, 20 Jul 2022 11:57:31 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Sheikh",
"Shakeel Ahmad",
""
],
[
"Sahidullah",
"Md",
""
],
[
"Hirsch",
"Fabrice",
""
],
[
"Ouni",
"Slim",
""
]
] |
new_dataset
| 0.991371 |
2207.10823
|
Keita Emura
|
Kota Chin, Keita Emura, Kazumasa Omote, Shingo Sato
|
A Sealed-bid Auction with Fund Binding: Preventing Maximum Bidding Price
Leakage
| null | null | null | null |
cs.CR cs.GT
|
http://creativecommons.org/licenses/by/4.0/
|
In an open-bid auction, a bidder can know the budgets of other bidders. Thus,
a sealed-bid auction that hides bidding prices is desirable. However, in
previous sealed-bid auction protocols, it has been difficult to provide a
``fund binding'' property, which would guarantee that a bidder has funds more
than or equal to the bidding price and that the funds are forcibly withdrawn
when the bidder wins. Thus, such protocols are vulnerable to false bidding. As
a solution, many protocols employ a simple deposit method in which each bidder
sends a deposit to a smart contract, which is greater than or equal to the
bidding price, before the bidding phase. However, this deposit reveals the
maximum bidding price, and it is preferable to hide this information.
In this paper, we propose a sealed-bid auction protocol that provides a fund
binding property. Our protocol not only hides the bidding price and a maximum
bidding price, but also provides fund binding, simultaneously. For hiding the
maximum bidding price, we pay attention to the fact that usual Ethereum
transactions and transactions for sending funds to a one-time address have the
same transaction structure, and it seems that they are indistinguishable. We
discuss how much bidding transactions are hidden. We also employ DECO (Zhang et
al,. CCS 2020) that proves the validity of the data to a verifier in which the
data are taken from a source without showing the data itself. Finally, we give
our implementation which shows transaction fees required and compare it to a
sealed-bid auction protocol employing the simple deposit method.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 00:15:02 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Chin",
"Kota",
""
],
[
"Emura",
"Keita",
""
],
[
"Omote",
"Kazumasa",
""
],
[
"Sato",
"Shingo",
""
]
] |
new_dataset
| 0.950245 |
2207.10931
|
Jonathan Bourne
|
Jonathan Bourne, Andrea Ingianni, Rex McKenzie
|
What's in the laundromat? Mapping and characterising offshore owned
domestic property in London
|
27 pages, 7 figures, 7 tables
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The UK, particularly London, is a global hub for money laundering, a
significant portion of which uses domestic property. However, understanding the
distribution and characteristics of offshore domestic property in the UK is
challenging due to data availability. This paper attempts to remedy that
situation by enhancing a publicly available dataset of UK property owned by
offshore companies. We create a data processing pipeline which draws on several
datasets and machine learning techniques to create a parsed set of addresses
classified into six use classes. The enhanced dataset contains 138,000
properties 44,000 more than the original dataset. The majority are domestic
(95k), with a disproportionate amount of those in London (42k). The average
offshore domestic property in London is worth 1.33 million GBP collectively
this amounts to approximately 56 Billion GBP. We perform an in-depth analysis
of the offshore domestic property in London, comparing the price, distribution
and entropy/concentration with Airbnb property, low-use/empty property and
conventional domestic property. We estimate that the total amount of offshore,
low-use and airbnb property in London is between 144,000 and 164,000 and that
they are collectively worth between 145-174 billion GBP. Furthermore, offshore
domestic property is more expensive and has higher entropy/concentration than
all other property types. In addition, we identify two different types of
offshore property, nested and individual, which have different price and
distribution characteristics. Finally, we release the enhanced offshore
property dataset, the complete low-use London dataset and the pipeline for
creating the enhanced dataset to reduce the barriers to studying this topic.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 08:08:21 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Bourne",
"Jonathan",
""
],
[
"Ingianni",
"Andrea",
""
],
[
"McKenzie",
"Rex",
""
]
] |
new_dataset
| 0.999488 |
2207.10950
|
Peter Naylor
|
Peter Naylor, Yao-Hung Hubert Tsai, Marick La\'e and Makoto Yamada
|
Scale dependant layer for self-supervised nuclei encoding
|
13 pages, 6 figures, 2 tables
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Recent developments in self-supervised learning give us the possibility to
further reduce human intervention in multi-step pipelines where the focus
evolves around particular objects of interest. In the present paper, the focus
lays in the nuclei in histopathology images. In particular we aim at extracting
cellular information in an unsupervised manner for a downstream task. As nuclei
present themselves in a variety of sizes, we propose a new Scale-dependant
convolutional layer to bypass scaling issues when resizing nuclei. On three
nuclei datasets, we benchmark the following methods: handcrafted, pre-trained
ResNet, supervised ResNet and self-supervised features. We show that the
proposed convolution layer boosts performance and that this layer combined with
Barlows-Twins allows for better nuclei encoding compared to the supervised
paradigm in the low sample setting and outperforms all other proposed
unsupervised methods. In addition, we extend the existing TNBC dataset to
incorporate nuclei class annotation in order to enrich and publicly release a
small sample setting dataset for nuclei segmentation and classification.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 08:56:57 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Naylor",
"Peter",
""
],
[
"Tsai",
"Yao-Hung Hubert",
""
],
[
"Laé",
"Marick",
""
],
[
"Yamada",
"Makoto",
""
]
] |
new_dataset
| 0.997104 |
2207.10953
|
Beichen Sun
|
Guanyu Zhang, Beichen Sun, Yuehan Qi, Yang Liu
|
Visible and Near Infrared Image Fusion Based on Texture Information
|
10 pages,11 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multi-sensor fusion is widely used in the environment perception system of
the autonomous vehicle. It solves the interference caused by environmental
changes and makes the whole driving system safer and more reliable. In this
paper, a novel visible and near-infrared fusion method based on texture
information is proposed to enhance unstructured environmental images. It aims
at the problems of artifact, information loss and noise in traditional visible
and near infrared image fusion methods. Firstly, the structure information of
the visible image (RGB) and the near infrared image (NIR) after texture removal
is obtained by relative total variation (RTV) calculation as the base layer of
the fused image; secondly, a Bayesian classification model is established to
calculate the noise weight and the noise information and the noise information
in the visible image is adaptively filtered by joint bilateral filter; finally,
the fused image is acquired by color space conversion. The experimental results
demonstrate that the proposed algorithm can preserve the spectral
characteristics and the unique information of visible and near-infrared images
without artifacts and color distortion, and has good robustness as well as
preserving the unique texture.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 09:02:17 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Zhang",
"Guanyu",
""
],
[
"Sun",
"Beichen",
""
],
[
"Qi",
"Yuehan",
""
],
[
"Liu",
"Yang",
""
]
] |
new_dataset
| 0.971805 |
2207.10955
|
Hang Ye
|
Hang Ye, Wentao Zhu, Chunyu Wang, Rujie Wu, Yizhou Wang
|
Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic
Projection
|
22 pages, 7 figures, submitted to ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While the voxel-based methods have achieved promising results for
multi-person 3D pose estimation from multi-cameras, they suffer from heavy
computation burdens, especially for large scenes. We present Faster VoxelPose
to address the challenge by re-projecting the feature volume to the three
two-dimensional coordinate planes and estimating X, Y, Z coordinates from them
separately. To that end, we first localize each person by a 3D bounding box by
estimating a 2D box and its height based on the volume features projected to
the xy-plane and z-axis, respectively. Then for each person, we estimate
partial joint coordinates from the three coordinate planes separately which are
then fused to obtain the final 3D pose. The method is free from costly 3D-CNNs
and improves the speed of VoxelPose by ten times and meanwhile achieves
competitive accuracy as the state-of-the-art methods, proving its potential in
real-time applications.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 09:10:01 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Ye",
"Hang",
""
],
[
"Zhu",
"Wentao",
""
],
[
"Wang",
"Chunyu",
""
],
[
"Wu",
"Rujie",
""
],
[
"Wang",
"Yizhou",
""
]
] |
new_dataset
| 0.997312 |
2207.11000
|
R\"udiger Ehlers
|
R\"udiger Ehlers and Sven Schewe
|
Natural Colors of Infinite Words
| null | null | null | null |
cs.FL cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While finite automata have minimal DFAs as a simple and natural normal form,
deterministic omega-automata do not currently have anything similar. One reason
for this is that a normal form for omega-regular languages has to speak about
more than acceptance - for example, to have a normal form for a parity
language, it should relate every infinite word to some natural color for this
language. This raises the question of whether or not a concept such as a
natural color of an infinite word (for a given language) exists, and, if it
does, how it relates back to automata.
We define the natural color of a word purely based on an omega-regular
language, and show how this natural color can be traced back from any
deterministic parity automaton after two cheap and simple automaton
transformations. The resulting streamlined automaton does not necessarily
accept every word with its natural color, but it has a 'co-run', which is like
a run, but can once move to a language equivalent state, whose color is the
natural color, and no co-run with a higher color exists.
The streamlined automaton defines, for every color c, a good-for-games
co-B\"uchi automaton that recognizes the words whose natural colors w.r.t. the
represented language are at least c. This provides a canonical representation
for every $\omega$-regular language, because good-for-games co-B\"uchi automata
have a canonical minimal (and cheap to obtain) representation for every
co-B\"uchi language.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 10:36:04 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Ehlers",
"Rüdiger",
""
],
[
"Schewe",
"Sven",
""
]
] |
new_dataset
| 0.99857 |
2207.11012
|
Francisca Pessanha
|
Francisca Pessanha, Gizem Sogancioglu
|
Fact sheet: Automatic Self-Reported Personality Recognition Track
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We propose an informed baseline to help disentangle the various contextual
factors of influence in this type of case studies. For this purpose, we
analysed the correlation between the given metadata and the self-assigned
personality trait scores and developed a model based solely on this
information. Further, we compared the performance of this informed baseline
with models based on state-of-the-art visual, linguistic and audio features.
For the present dataset, a model trained solely on simple metadata features
(age, gender and number of sessions) proved to have superior or similar
performance when compared with simple audio, linguistic or visual
features-based systems.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 11:30:11 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Pessanha",
"Francisca",
""
],
[
"Sogancioglu",
"Gizem",
""
]
] |
new_dataset
| 0.995429 |
2207.11031
|
Mohammad Hajizadeh Saffar
|
Mohammad Hajizadeh, Mohammad Sabokrou, Adel Rahmani
|
MobileDenseNet: A new approach to object detection on mobile devices
| null | null | null | null |
cs.CV cs.LG cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
Object detection problem solving has developed greatly within the past few
years. There is a need for lighter models in instances where hardware
limitations exist, as well as a demand for models to be tailored to mobile
devices. In this article, we will assess the methods used when creating
algorithms that address these issues. The main goal of this article is to
increase accuracy in state-of-the-art algorithms while maintaining speed and
real-time efficiency. The most significant issues in one-stage object detection
pertains to small objects and inaccurate localization. As a solution, we
created a new network by the name of MobileDenseNet suitable for embedded
systems. We also developed a light neck FCPNLite for mobile devices that will
aid with the detection of small objects. Our research revealed that very few
papers cited necks in embedded systems. What differentiates our network from
others is our use of concatenation features. A small yet significant change to
the head of the network amplified accuracy without increasing speed or limiting
parameters. In short, our focus on the challenging CoCo and Pascal VOC datasets
were 24.8 and 76.8 in percentage terms respectively - a rate higher than that
recorded by other state-of-the-art systems thus far. Our network is able to
increase accuracy while maintaining real-time efficiency on mobile devices. We
calculated operational speed on Pixel 3 (Snapdragon 845) to 22.8 fps. The
source code of this research is available on
https://github.com/hajizadeh/MobileDenseNet.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 12:13:59 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Hajizadeh",
"Mohammad",
""
],
[
"Sabokrou",
"Mohammad",
""
],
[
"Rahmani",
"Adel",
""
]
] |
new_dataset
| 0.993052 |
2207.11082
|
Matias Martinez
|
Matias Martinez, Maria Kechagia, Anjana Perera, Justyna Petke,
Federica Sarro and Aldeida Aleti
|
Test-based Patch Clustering for Automatically-Generated Patches
Assessment
| null | null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Previous studies have shown that Automated Program Repair (APR) techniques
suffer from the overfitting problem. Overfitting happens when a patch is run
and the test suite does not reveal any error, but the patch actually does not
fix the underlying bug or it introduces a new defect that is not covered by the
test suite. Therefore, the patches generated by APR tools need to be validated
by human programmers, which can be very costly, and prevents APR tools adoption
in practice.Our work aims at increasing developer trust in automated patch
generation by minimizing the number of plausible patches that they have to
review, thereby reducing the time required to find a correct patch. We
introduce a novel light-weight test-based patch clustering approach called
xTestCluster, which clusters patches based on their dynamic behavior.
xTestCluster is applied after the patch generation phase in order to analyze
the generated patches from one or more repair tools. The novelty of
xTestCluster lies in using information from execution of newly generated test
cases to cluster patches generated by multiple APR approaches. A cluster is
formed with patches that fail on the same generated test cases. The output from
xTestCluster gives developers a) a way of reducing the number of patches to
analyze, as they can focus on analyzing a sample of patches from each cluster,
b) additional information attached to each patch. After analyzing 1910
plausible patches from 25 Java APR tools, our results show that xTestCluster is
able to reduce the number of patches to review and analyze with a median of
50%. xTestCluster can save a significant amount of time for developers that
have to review the multitude of patches generated by APR tools, and provides
them with new test cases that show the differences in behavior between
generated patches.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 13:39:27 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Martinez",
"Matias",
""
],
[
"Kechagia",
"Maria",
""
],
[
"Perera",
"Anjana",
""
],
[
"Petke",
"Justyna",
""
],
[
"Sarro",
"Federica",
""
],
[
"Aleti",
"Aldeida",
""
]
] |
new_dataset
| 0.958018 |
2207.11146
|
Abdallah Chehade
|
Mayuresh Savargaonkar and Abdallah Chehade
|
VTrackIt: A Synthetic Self-Driving Dataset with Infrastructure and
Pooled Vehicle Information
| null | null | null | null |
cs.CV cs.AI cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Artificial intelligence solutions for Autonomous Vehicles (AVs) have been
developed using publicly available datasets such as Argoverse, ApolloScape,
Level5, and NuScenes. One major limitation of these datasets is the absence of
infrastructure and/or pooled vehicle information like lane line type, vehicle
speed, traffic signs, and intersections. Such information is necessary and not
complementary to eliminating high-risk edge cases. The rapid advancements in
Vehicle-to-Infrastructure and Vehicle-to-Vehicle technologies show promise that
infrastructure and pooled vehicle information will soon be accessible in near
real-time. Taking a leap in the future, we introduce the first comprehensive
synthetic dataset with intelligent infrastructure and pooled vehicle
information for advancing the next generation of AVs, named VTrackIt. We also
introduce the first deep learning model (InfraGAN) for trajectory predictions
that considers such information. Our experiments with InfraGAN show that the
comprehensive information offered by VTrackIt reduces the number of high-risk
edge cases. The VTrackIt dataset is available upon request under the Creative
Commons CC BY-NC-SA 4.0 license at http://vtrackit.irda.club.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 16:00:33 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Savargaonkar",
"Mayuresh",
""
],
[
"Chehade",
"Abdallah",
""
]
] |
new_dataset
| 0.999818 |
2207.11230
|
Thibault Clerice
|
Thibault Cl\'erice (ENC, CJM, HiSoMA, UJML)
|
You Actually Look Twice At it (YALTAi): using an object detection
approach instead of region segmentation within the Kraken engine
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Layout Analysis (the identification of zones and their classification) is the
first step along line segmentation in Optical Character Recognition and similar
tasks. The ability of identifying main body of text from marginal text or
running titles makes the difference between extracting the work full text of a
digitized book and noisy outputs. We show that most segmenters focus on pixel
classification and that polygonization of this output has not been used as a
target for the latest competition on historical document (ICDAR 2017 and
onwards), despite being the focus in the early 2010s. We propose to shift, for
efficiency, the task from a pixel classification-based polygonization to an
object detection using isothetic rectangles. We compare the output of Kraken
and YOLOv5 in terms of segmentation and show that the later severely
outperforms the first on small datasets (1110 samples and below). We release
two datasets for training and evaluation on historical documents as well as a
new package, YALTAi, which injects YOLOv5 in the segmentation pipeline of
Kraken 4.1.
|
[
{
"version": "v1",
"created": "Tue, 19 Jul 2022 07:50:16 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Clérice",
"Thibault",
"",
"ENC, CJM, HiSoMA, UJML"
]
] |
new_dataset
| 0.951512 |
2207.11236
|
Andreas Buchmueller
|
Andreas Buchm\"uller, Gillian Kant, Christoph Weisser, Benjamin
S\"afken, Krisztina Kis-Katos, Thomas Kneib
|
Twitmo: A Twitter Data Topic Modeling and Visualization Package for R
|
16 pages, 4 figures
| null | null | null |
cs.IR cs.CL cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
We present Twitmo, a package that provides a broad range of methods to
collect, pre-process, analyze and visualize geo-tagged Twitter data. Twitmo
enables the user to collect geo-tagged Tweets from Twitter and and provides a
comprehensive and user-friendly toolbox to generate topic distributions from
Latent Dirichlet Allocations (LDA), correlated topic models (CTM) and
structural topic models (STM). Functions are included for pre-processing of
text, model building and prediction. In addition, one of the innovations of the
package is the automatic pooling of Tweets into longer pseudo-documents using
hashtags and cosine similarities for better topic coherence. The package
additionally comes with functionality to visualize collected data sets and
fitted models in static as well as interactive ways and offers built-in support
for model visualizations via LDAvis providing great convenience for researchers
in this area. The Twitmo package is an innovative toolbox that can be used to
analyze public discourse of various topics, political parties or persons of
interest in space and time.
|
[
{
"version": "v1",
"created": "Fri, 8 Jul 2022 12:23:20 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Buchmüller",
"Andreas",
""
],
[
"Kant",
"Gillian",
""
],
[
"Weisser",
"Christoph",
""
],
[
"Säfken",
"Benjamin",
""
],
[
"Kis-Katos",
"Krisztina",
""
],
[
"Kneib",
"Thomas",
""
]
] |
new_dataset
| 0.999425 |
2207.11247
|
Jingkang Yang
|
Jingkang Yang, Yi Zhe Ang, Zujin Guo, Kaiyang Zhou, Wayne Zhang, and
Ziwei Liu
|
Panoptic Scene Graph Generation
|
Accepted to ECCV'22 (Paper ID #222, Final Score 2222). Project Page:
https://psgdataset.org/. OpenPSG Codebase:
https://github.com/Jingkang50/OpenPSG
| null | null | null |
cs.CV cs.AI cs.CL cs.LG cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
Existing research addresses scene graph generation (SGG) -- a critical
technology for scene understanding in images -- from a detection perspective,
i.e., objects are detected using bounding boxes followed by prediction of their
pairwise relationships. We argue that such a paradigm causes several problems
that impede the progress of the field. For instance, bounding box-based labels
in current datasets usually contain redundant classes like hairs, and leave out
background information that is crucial to the understanding of context. In this
work, we introduce panoptic scene graph generation (PSG), a new problem task
that requires the model to generate a more comprehensive scene graph
representation based on panoptic segmentations rather than rigid bounding
boxes. A high-quality PSG dataset, which contains 49k well-annotated
overlapping images from COCO and Visual Genome, is created for the community to
keep track of its progress. For benchmarking, we build four two-stage
baselines, which are modified from classic methods in SGG, and two one-stage
baselines called PSGTR and PSGFormer, which are based on the efficient
Transformer-based detector, i.e., DETR. While PSGTR uses a set of queries to
directly learn triplets, PSGFormer separately models the objects and relations
in the form of queries from two Transformer decoders, followed by a
prompting-like relation-object matching mechanism. In the end, we share
insights on open challenges and future directions.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 17:59:53 GMT"
}
] | 2022-07-25T00:00:00 |
[
[
"Yang",
"Jingkang",
""
],
[
"Ang",
"Yi Zhe",
""
],
[
"Guo",
"Zujin",
""
],
[
"Zhou",
"Kaiyang",
""
],
[
"Zhang",
"Wayne",
""
],
[
"Liu",
"Ziwei",
""
]
] |
new_dataset
| 0.974453 |
2101.08238
|
Ammarah Farooq
|
Ammarah Farooq, Muhammad Awais, Josef Kittler, Syed Safwan Khalid
|
AXM-Net: Implicit Cross-Modal Feature Alignment for Person
Re-identification
|
AAAI-2022 (Oral Paper)
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cross-modal person re-identification (Re-ID) is critical for modern video
surveillance systems. The key challenge is to align cross-modality
representations induced by the semantic information present for a person and
ignore background information. This work presents a novel convolutional neural
network (CNN) based architecture designed to learn semantically aligned
cross-modal visual and textual representations. The underlying building block,
named AXM-Block, is a unified multi-layer network that dynamically exploits the
multi-scale knowledge from both modalities and re-calibrates each modality
according to shared semantics. To complement the convolutional design,
contextual attention is applied in the text branch to manipulate long-term
dependencies. Moreover, we propose a unique design to enhance visual part-based
feature coherence and locality information. Our framework is novel in its
ability to implicitly learn aligned semantics between modalities during the
feature learning stage. The unified feature learning effectively utilizes
textual data as a super-annotation signal for visual representation learning
and automatically rejects irrelevant information. The entire AXM-Net is trained
end-to-end on CUHK-PEDES data. We report results on two tasks, person search
and cross-modal Re-ID. The AXM-Net outperforms the current state-of-the-art
(SOTA) methods and achieves 64.44\% Rank@1 on the CUHK-PEDES test set. It also
outperforms its competitors by $>$10\% in cross-viewpoint text-to-image Re-ID
scenarios on CrossRe-ID and CUHK-SYSU datasets.
|
[
{
"version": "v1",
"created": "Tue, 19 Jan 2021 16:06:39 GMT"
},
{
"version": "v2",
"created": "Fri, 19 Mar 2021 15:28:49 GMT"
},
{
"version": "v3",
"created": "Wed, 20 Jul 2022 23:20:12 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Farooq",
"Ammarah",
""
],
[
"Awais",
"Muhammad",
""
],
[
"Kittler",
"Josef",
""
],
[
"Khalid",
"Syed Safwan",
""
]
] |
new_dataset
| 0.973104 |
2107.11020
|
Junyi Jessy Li
|
Tiberiu Sosea, Chau Pham, Alexander Tekle, Cornelia Caragea, Junyi
Jessy Li
|
Emotion analysis and detection during COVID-19
|
LREC 2022
| null | null | null |
cs.CL cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Crises such as natural disasters, global pandemics, and social unrest
continuously threaten our world and emotionally affect millions of people
worldwide in distinct ways. Understanding emotions that people express during
large-scale crises helps inform policy makers and first responders about the
emotional states of the population as well as provide emotional support to
those who need such support. We present CovidEmo, ~3K English tweets labeled
with emotions and temporally distributed across 18 months. Our analyses reveal
the emotional toll caused by COVID-19, and changes of the social narrative and
associated emotions over time. Motivated by the time-sensitive nature of crises
and the cost of large-scale annotation efforts, we examine how well large
pre-trained language models generalize across domains and timeline in the task
of perceived emotion prediction in the context of COVID-19. Our analyses
suggest that cross-domain information transfers occur, yet there are still
significant gaps. We propose semi-supervised learning as a way to bridge this
gap, obtaining significantly better performance using unlabeled data from the
target domain.
|
[
{
"version": "v1",
"created": "Fri, 23 Jul 2021 04:07:14 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Oct 2021 22:08:59 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Jul 2022 02:16:07 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Sosea",
"Tiberiu",
""
],
[
"Pham",
"Chau",
""
],
[
"Tekle",
"Alexander",
""
],
[
"Caragea",
"Cornelia",
""
],
[
"Li",
"Junyi Jessy",
""
]
] |
new_dataset
| 0.996614 |
2108.13327
|
Zhen Wang
|
Zhen Wang, Xu Shan, Xiangxie Zhang, Jie Yang
|
N24News: A New Dataset for Multimodal News Classification
| null |
Proceedings of the 13th Conference on Language Resources and
Evaluation (LREC 2022), pages 6768-6775
| null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current news datasets merely focus on text features on the news and rarely
leverage the feature of images, excluding numerous essential features for news
classification. In this paper, we propose a new dataset, N24News, which is
generated from New York Times with 24 categories and contains both text and
image information in each news. We use a multitask multimodal method and the
experimental results show multimodal news classification performs better than
text-only news classification. Depending on the length of the text, the
classification accuracy can be increased by up to 8.11%. Our research reveals
the relationship between the performance of a multimodal classifier and its
sub-classifiers, and also the possible improvements when applying multimodal in
news classification. N24News is shown to have great potential to prompt the
multimodal news studies.
|
[
{
"version": "v1",
"created": "Mon, 30 Aug 2021 15:46:09 GMT"
},
{
"version": "v2",
"created": "Tue, 16 Nov 2021 15:14:14 GMT"
},
{
"version": "v3",
"created": "Fri, 17 Dec 2021 15:20:11 GMT"
},
{
"version": "v4",
"created": "Mon, 6 Jun 2022 06:51:28 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Wang",
"Zhen",
""
],
[
"Shan",
"Xu",
""
],
[
"Zhang",
"Xiangxie",
""
],
[
"Yang",
"Jie",
""
]
] |
new_dataset
| 0.999883 |
2109.07775
|
Marcell Missura
|
Marcell Missura, Arindam Roychoudhury and Maren Bennewitz
|
Fast-Replanning Motion Control for Non-Holonomic Vehicles with Aborting
A*
|
Accepted to IROS 22
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Autonomously driving vehicles must be able to navigate in dynamic and
unpredictable environments in a collision-free manner. So far, this has only
been partially achieved in driverless cars and warehouse installations where
marked structures such as roads, lanes, and traffic signs simplify the motion
planning and collision avoidance problem. We are presenting a new control
approach for car-like vehicles that is based on an unprecedentedly fast-paced
A* implementation that allows the control cycle to run at a frequency of 30 Hz.
This frequency enables us to place our A* algorithm as a low-level replanning
controller that is well suited for navigation and collision avoidance in
virtually any dynamic environment. Due to an efficient heuristic consisting of
rotate-translate-rotate motions laid out along the shortest path to the target,
our Short-Term Aborting A* (STAA*) converges fast and can be aborted early in
order to guarantee a high and steady control rate. While our STAA* expands
states along the shortest path, it takes care of collision checking with the
environment including predicted states of moving obstacles, and returns the
best solution found when the computation time runs out. Despite the bounded
computation time, our STAA* does not get trapped in corners due to the
following of the shortest path. In simulated and real-robot experiments, we
demonstrate that our control approach eliminates collisions almost entirely and
is superior to an improved version of the Dynamic Window Approach with
predictive collision avoidance capabilities.
|
[
{
"version": "v1",
"created": "Thu, 16 Sep 2021 07:51:26 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Mar 2022 10:22:54 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Jul 2022 12:47:46 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Missura",
"Marcell",
""
],
[
"Roychoudhury",
"Arindam",
""
],
[
"Bennewitz",
"Maren",
""
]
] |
new_dataset
| 0.999516 |
2110.00058
|
Erik Demaine
|
Erik D. Demaine and Maarten L\"offler and Christiane Schmidt
|
Rectangular Spiral Galaxies are Still Hard
|
24 pages, 24 figures. Thorough revision including new Section 2 proof
which handles the promise problem
| null | null | null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Spiral Galaxies is a pencil-and-paper puzzle played on a grid of unit
squares: given a set of points called centers, the goal is to partition the
grid into polyominoes such that each polyomino contains exactly one center and
is 180{\deg} rotationally symmetric about its center. We show that this puzzle
is NP-complete, ASP-complete, and #P-complete even if (a) all solutions to the
puzzle have rectangles for polyominoes; or (b) the polyominoes are required to
be rectangles and all solutions to the puzzle have just 1$\times$1, 1$\times$3,
and 3$\times$1 rectangles. The proof for the latter variant also implies
NP/ASP/#P-completeness of finding a noncrossing perfect matching in distance-2
grid graphs where edges connect vertices of Euclidean distance 2. Moreover, we
prove NP-completeness of the design problem of minimizing the number of centers
such that there exists a set of galaxies that exactly cover a given shape
|
[
{
"version": "v1",
"created": "Thu, 30 Sep 2021 19:33:32 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Jul 2022 18:00:16 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Demaine",
"Erik D.",
""
],
[
"Löffler",
"Maarten",
""
],
[
"Schmidt",
"Christiane",
""
]
] |
new_dataset
| 0.998998 |
2110.14284
|
Christian Frey
|
Christian M.M. Frey, Yunpu Ma, Matthias Schubert
|
APPTeK: Agent-Based Predicate Prediction in Temporal Knowledge Graphs
| null | null | null | null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In temporal Knowledge Graphs (tKGs), the temporal dimension is attached to
facts in a knowledge base resulting in quadruples between entities such as
(Nintendo, released, Super Mario, Sep-13-1985), where the predicate holds
within a time interval or at a timestamp. We propose a reinforcement learning
agent gathering temporal relevant information about the query entities'
neighborhoods, simultaneously. We refer to the encodings of the explored graph
structures as fingerprints which are used as input to a Q-network. Our agent
decides sequentially which relation type needs to be explored next to expand
the local subgraphs of the query entities. Our evaluation shows that the
proposed method yields competitive results compared to state-of-the-art
embedding algorithms for tKGs, and we additionally gain information about the
relevant structures between subjects and objects.
|
[
{
"version": "v1",
"created": "Wed, 27 Oct 2021 09:05:23 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 07:58:21 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Frey",
"Christian M. M.",
""
],
[
"Ma",
"Yunpu",
""
],
[
"Schubert",
"Matthias",
""
]
] |
new_dataset
| 0.998209 |
2111.05610
|
Zijian Gao
|
Zijian Gao, Jingyu Liu, Weiqi Sun, Sheng Chen, Dedan Chang, Lili Zhao
|
CLIP2TV: Align, Match and Distill for Video-Text Retrieval
| null | null | null | null |
cs.CV cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Modern video-text retrieval frameworks basically consist of three parts:
video encoder, text encoder and the similarity head. With the success on both
visual and textual representation learning, transformer based encoders and
fusion methods have also been adopted in the field of video-text retrieval. In
this report, we present CLIP2TV, aiming at exploring where the critical
elements lie in transformer based methods. To achieve this, We first revisit
some recent works on multi-modal learning, then introduce some techniques into
video-text retrieval, finally evaluate them through extensive experiments in
different configurations. Notably, CLIP2TV achieves 52.9@R1 on MSR-VTT dataset,
outperforming the previous SOTA result by 4.1%.
|
[
{
"version": "v1",
"created": "Wed, 10 Nov 2021 10:05:11 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 17:19:19 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Gao",
"Zijian",
""
],
[
"Liu",
"Jingyu",
""
],
[
"Sun",
"Weiqi",
""
],
[
"Chen",
"Sheng",
""
],
[
"Chang",
"Dedan",
""
],
[
"Zhao",
"Lili",
""
]
] |
new_dataset
| 0.99403 |
2111.07640
|
Sunghyun Park
|
Kangyeol Kim, Sunghyun Park, Jaeseong Lee, Sunghyo Chung, Junsoo Lee,
Jaegul Choo
|
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head
Reenactment
|
40 pages; Accepted to ECCV 2022; code and dataset URL added
| null | null | null |
cs.AI cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an
animation head reenactment. Different from previous animation head datasets, we
utilize 3D animation models as the controllable image samplers, which can
provide a large amount of head images with their corresponding detailed pose
annotations. To facilitate a data creation process, we build a semi-automatic
pipeline leveraging an open 3D computer graphics software with a developed
annotation system. After training with the AnimeCeleb, recent head reenactment
models produce high-quality animation head reenactment results, which are not
achievable with existing datasets. Furthermore, motivated by metaverse
application, we propose a novel pose mapping method and architecture to tackle
a cross-domain head reenactment task. During inference, a user can easily
transfer one's motion to an arbitrary animation head. Experiments demonstrate
the usefulness of the AnimeCeleb to train animation head reenactment models,
and the superiority of our cross-domain head reenactment model compared to
state-of-the-art methods. Our dataset and code are available at
https://github.com/kangyeolk/AnimeCeleb.
|
[
{
"version": "v1",
"created": "Mon, 15 Nov 2021 10:00:06 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 07:49:29 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Kim",
"Kangyeol",
""
],
[
"Park",
"Sunghyun",
""
],
[
"Lee",
"Jaeseong",
""
],
[
"Chung",
"Sunghyo",
""
],
[
"Lee",
"Junsoo",
""
],
[
"Choo",
"Jaegul",
""
]
] |
new_dataset
| 0.999864 |
2112.08775
|
Jaewoo Park
|
Jaewoo Park, Nam Ik Cho
|
DProST: Dynamic Projective Spatial Transformer Network for 6D Pose
Estimation
|
Accepted to ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Predicting the object's 6D pose from a single RGB image is a fundamental
computer vision task. Generally, the distance between transformed object
vertices is employed as an objective function for pose estimation methods.
However, projective geometry in the camera space is not considered in those
methods and causes performance degradation. In this regard, we propose a new
pose estimation system based on a projective grid instead of object vertices.
Our pose estimation method, dynamic projective spatial transformer network
(DProST), localizes the region of interest grid on the rays in camera space and
transforms the grid to object space by estimated pose. The transformed grid is
used as both a sampling grid and a new criterion of the estimated pose.
Additionally, because DProST does not require object vertices, our method can
be used in a mesh-less setting by replacing the mesh with a reconstructed
feature. Experimental results show that mesh-less DProST outperforms the
state-of-the-art mesh-based methods on the LINEMOD and LINEMOD-OCCLUSION
dataset, and shows competitive performance on the YCBV dataset with mesh data.
The source code is available at https://github.com/parkjaewoo0611/DProST
|
[
{
"version": "v1",
"created": "Thu, 16 Dec 2021 10:39:09 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 10:48:49 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Park",
"Jaewoo",
""
],
[
"Cho",
"Nam Ik",
""
]
] |
new_dataset
| 0.997958 |
2112.13715
|
Ailing Zeng
|
Ailing Zeng, Lei Yang, Xuan Ju, Jiefeng Li, Jianyi Wang, Qiang Xu
|
SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos
|
Accepted by ECCV 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
When analyzing human motion videos, the output jitters from existing pose
estimators are highly-unbalanced with varied estimation errors across frames.
Most frames in a video are relatively easy to estimate and only suffer from
slight jitters. In contrast, for rarely seen or occluded actions, the estimated
positions of multiple joints largely deviate from the ground truth values for a
consecutive sequence of frames, rendering significant jitters on them. To
tackle this problem, we propose to attach a dedicated temporal-only refinement
network to existing pose estimators for jitter mitigation, named SmoothNet.
Unlike existing learning-based solutions that employ spatio-temporal models to
co-optimize per-frame precision and temporal smoothness at all the joints,
SmoothNet models the natural smoothness characteristics in body movements by
learning the long-range temporal relations of every joint without considering
the noisy correlations among joints. With a simple yet effective motion-aware
fully-connected network, SmoothNet improves the temporal smoothness of existing
pose estimators significantly and enhances the estimation accuracy of those
challenging frames as a side-effect. Moreover, as a temporal-only model, a
unique advantage of SmoothNet is its strong transferability across various
types of estimators and datasets. Comprehensive experiments on five datasets
with eleven popular backbone networks across 2D and 3D pose estimation and body
recovery tasks demonstrate the efficacy of the proposed solution. Code is
available at https://github.com/cure-lab/SmoothNet.
|
[
{
"version": "v1",
"created": "Mon, 27 Dec 2021 14:53:30 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 17:15:06 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Zeng",
"Ailing",
""
],
[
"Yang",
"Lei",
""
],
[
"Ju",
"Xuan",
""
],
[
"Li",
"Jiefeng",
""
],
[
"Wang",
"Jianyi",
""
],
[
"Xu",
"Qiang",
""
]
] |
new_dataset
| 0.995487 |
2202.08771
|
Jaesung Rim
|
Jaesung Rim, Geonung Kim, Jungeon Kim, Junyong Lee, Seungyong Lee,
Sunghyun Cho
|
Realistic Blur Synthesis for Learning Image Deblurring
|
ECCV 2022,Project page: http://cg.postech.ac.kr/research/rsblur/
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Training learning-based deblurring methods demands a tremendous amount of
blurred and sharp image pairs. Unfortunately, existing synthetic datasets are
not realistic enough, and deblurring models trained on them cannot handle real
blurred images effectively. While real datasets have recently been proposed,
they provide limited diversity of scenes and camera settings, and capturing
real datasets for diverse settings is still challenging. To resolve this, this
paper analyzes various factors that introduce differences between real and
synthetic blurred images. To this end, we present RSBlur, a novel dataset with
real blurred images and the corresponding sharp image sequences to enable a
detailed analysis of the difference between real and synthetic blur. With the
dataset, we reveal the effects of different factors in the blur generation
process. Based on the analysis, we also present a novel blur synthesis pipeline
to synthesize more realistic blur. We show that our synthesis pipeline can
improve the deblurring performance on real blurred images.
|
[
{
"version": "v1",
"created": "Thu, 17 Feb 2022 17:14:48 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Apr 2022 22:23:59 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Jul 2022 06:05:08 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Rim",
"Jaesung",
""
],
[
"Kim",
"Geonung",
""
],
[
"Kim",
"Jungeon",
""
],
[
"Lee",
"Junyong",
""
],
[
"Lee",
"Seungyong",
""
],
[
"Cho",
"Sunghyun",
""
]
] |
new_dataset
| 0.996176 |
2203.02113
|
Pinaki Nath Chowdhury
|
Pinaki Nath Chowdhury and Aneeshan Sain and Ayan Kumar Bhunia and Tao
Xiang and Yulia Gryaditskaya and Yi-Zhe Song
|
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in
Context
|
Accepted in ECCV 2022. Project Page: https://fscoco.github.io
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We advance sketch research to scenes with the first dataset of freehand scene
sketches, FS-COCO. With practical applications in mind, we collect sketches
that convey scene content well but can be sketched within a few minutes by a
person with any sketching skills. Our dataset comprises 10,000 freehand scene
vector sketches with per point space-time information by 100 non-expert
individuals, offering both object- and scene-level abstraction. Each sketch is
augmented with its text description. Using our dataset, we study for the first
time the problem of fine-grained image retrieval from freehand scene sketches
and sketch captions. We draw insights on: (i) Scene salience encoded in
sketches using the strokes temporal order; (ii) Performance comparison of image
retrieval from a scene sketch and an image caption; (iii) Complementarity of
information in sketches and image captions, as well as the potential benefit of
combining the two modalities. In addition, we extend a popular vector sketch
LSTM-based encoder to handle sketches with larger complexity than was supported
by previous work. Namely, we propose a hierarchical sketch decoder, which we
leverage at a sketch-specific "pre-text" task. Our dataset enables for the
first time research on freehand scene sketch understanding and its practical
applications.
|
[
{
"version": "v1",
"created": "Fri, 4 Mar 2022 03:00:51 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Mar 2022 20:59:28 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Jul 2022 02:46:15 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Chowdhury",
"Pinaki Nath",
""
],
[
"Sain",
"Aneeshan",
""
],
[
"Bhunia",
"Ayan Kumar",
""
],
[
"Xiang",
"Tao",
""
],
[
"Gryaditskaya",
"Yulia",
""
],
[
"Song",
"Yi-Zhe",
""
]
] |
new_dataset
| 0.999328 |
2203.03890
|
Xiaotong Chen
|
Xiaotong Chen, Huijie Zhang, Zeren Yu, Anthony Opipari, Odest
Chadwicke Jenkins
|
ClearPose: Large-scale Transparent Object Dataset and Benchmark
|
ECCV 2022 accepted paper
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Transparent objects are ubiquitous in household settings and pose distinct
challenges for visual sensing and perception systems. The optical properties of
transparent objects leave conventional 3D sensors alone unreliable for object
depth and pose estimation. These challenges are highlighted by the shortage of
large-scale RGB-Depth datasets focusing on transparent objects in real-world
settings. In this work, we contribute a large-scale real-world RGB-Depth
transparent object dataset named ClearPose to serve as a benchmark dataset for
segmentation, scene-level depth completion and object-centric pose estimation
tasks. The ClearPose dataset contains over 350K labeled real-world RGB-Depth
frames and 5M instance annotations covering 63 household objects. The dataset
includes object categories commonly used in daily life under various lighting
and occluding conditions as well as challenging test scenarios such as cases of
occlusion by opaque or translucent objects, non-planar orientations, presence
of liquids, etc. We benchmark several state-of-the-art depth completion and
object pose estimation deep neural networks on ClearPose. The dataset and
benchmarking source code is available at https://github.com/opipari/ClearPose.
|
[
{
"version": "v1",
"created": "Tue, 8 Mar 2022 07:29:31 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 02:33:01 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Chen",
"Xiaotong",
""
],
[
"Zhang",
"Huijie",
""
],
[
"Yu",
"Zeren",
""
],
[
"Opipari",
"Anthony",
""
],
[
"Jenkins",
"Odest Chadwicke",
""
]
] |
new_dataset
| 0.999752 |
2203.08713
|
Ailing Zeng
|
Ailing Zeng, Xuan Ju, Lei Yang, Ruiyuan Gao, Xizhou Zhu, Bo Dai, Qiang
Xu
|
DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation
|
Accepted by ECCV 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This paper proposes a simple baseline framework for video-based 2D/3D human
pose estimation that can achieve 10 times efficiency improvement over existing
works without any performance degradation, named DeciWatch. Unlike current
solutions that estimate each frame in a video, DeciWatch introduces a simple
yet effective sample-denoise-recover framework that only watches sparsely
sampled frames, taking advantage of the continuity of human motions and the
lightweight pose representation. Specifically, DeciWatch uniformly samples less
than 10% video frames for detailed estimation, denoises the estimated 2D/3D
poses with an efficient Transformer architecture, and then accurately recovers
the rest of the frames using another Transformer-based network. Comprehensive
experimental results on three video-based human pose estimation and body mesh
recovery tasks with four datasets validate the efficiency and effectiveness of
DeciWatch. Code is available at https://github.com/cure-lab/DeciWatch.
|
[
{
"version": "v1",
"created": "Wed, 16 Mar 2022 16:03:37 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Jul 2022 18:02:53 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Zeng",
"Ailing",
""
],
[
"Ju",
"Xuan",
""
],
[
"Yang",
"Lei",
""
],
[
"Gao",
"Ruiyuan",
""
],
[
"Zhu",
"Xizhou",
""
],
[
"Dai",
"Bo",
""
],
[
"Xu",
"Qiang",
""
]
] |
new_dataset
| 0.99564 |
2203.10157
|
Jon\'a\v{s} Kulh\'anek
|
Jon\'a\v{s} Kulh\'anek and Erik Derner and Torsten Sattler and Robert
Babu\v{s}ka
|
ViewFormer: NeRF-free Neural Rendering from Few Images Using
Transformers
|
ECCV 2022 poster
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Novel view synthesis is a long-standing problem. In this work, we consider a
variant of the problem where we are given only a few context views sparsely
covering a scene or an object. The goal is to predict novel viewpoints in the
scene, which requires learning priors. The current state of the art is based on
Neural Radiance Field (NeRF), and while achieving impressive results, the
methods suffer from long training times as they require evaluating millions of
3D point samples via a neural network for each image. We propose a 2D-only
method that maps multiple context views and a query pose to a new image in a
single pass of a neural network. Our model uses a two-stage architecture
consisting of a codebook and a transformer model. The codebook is used to embed
individual images into a smaller latent space, and the transformer solves the
view synthesis task in this more compact space. To train our model efficiently,
we introduce a novel branching attention mechanism that allows us to use the
same model not only for neural rendering but also for camera pose estimation.
Experimental results on real-world scenes show that our approach is competitive
compared to NeRF-based methods while not reasoning explicitly in 3D, and it is
faster to train.
|
[
{
"version": "v1",
"created": "Fri, 18 Mar 2022 21:08:23 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 06:03:51 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Kulhánek",
"Jonáš",
""
],
[
"Derner",
"Erik",
""
],
[
"Sattler",
"Torsten",
""
],
[
"Babuška",
"Robert",
""
]
] |
new_dataset
| 0.967527 |
2204.03039
|
Yilun Chen
|
Yilun Chen, Shijia Huang, Shu Liu, Bei Yu, Jiaya Jia
|
DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors
|
13 pages
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Camera-based 3D object detectors are welcome due to their wider deployment
and lower price than LiDAR sensors. We first revisit the prior stereo detector
DSGN for its stereo volume construction ways for representing both 3D geometry
and semantics. We polish the stereo modeling and propose the advanced version,
DSGN++, aiming to enhance effective information flow throughout the 2D-to-3D
pipeline in three main aspects. First, to effectively lift the 2D information
to stereo volume, we propose depth-wise plane sweeping (DPS) that allows denser
connections and extracts depth-guided features. Second, for grasping
differently spaced features, we present a novel stereo volume -- Dual-view
Stereo Volume (DSV) that integrates front-view and top-view features and
reconstructs sub-voxel depth in the camera frustum. Third, as the foreground
region becomes less dominant in 3D space, we propose a multi-modal data editing
strategy -- Stereo-LiDAR Copy-Paste, which ensures cross-modal alignment and
improves data efficiency. Without bells and whistles, extensive experiments in
various modality setups on the popular KITTI benchmark show that our method
consistently outperforms other camera-based 3D detectors for all categories.
Code is available at https://github.com/chenyilun95/DSGN2.
|
[
{
"version": "v1",
"created": "Wed, 6 Apr 2022 18:43:54 GMT"
},
{
"version": "v2",
"created": "Sat, 9 Apr 2022 16:58:18 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Jul 2022 12:08:06 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Chen",
"Yilun",
""
],
[
"Huang",
"Shijia",
""
],
[
"Liu",
"Shu",
""
],
[
"Yu",
"Bei",
""
],
[
"Jia",
"Jiaya",
""
]
] |
new_dataset
| 0.987085 |
2204.12103
|
Junjie Zhang
|
Junjie Zhang, Amir Khodabandeh, Kourosh Khoshelham
|
Centimeter-level Positioning by Instantaneous Lidar-aided GNSS Ambiguity
Resolution
|
14 pages, 12 figures. Submitted to Measurement Science and Technology
| null |
10.1088/1361-6501/ac82dd
| null |
cs.RO eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
High-precision vehicle positioning is key to the implementation of modern
driving systems in urban environments. Global Navigation Satellite System
(GNSS) carrier phase measurements can provide millimeter- to centimeter-level
positioning, provided that the integer ambiguities are correctly resolved.
Abundant code measurements are often used to facilitate integer ambiguity
resolution (IAR), however, they suffer from signal blockage and multipath in
urban canyons. In this contribution, a lidar-aided instantaneous ambiguity
resolution method is proposed. Lidar measurements, in the form of 3D keypoints,
are generated by a learning-based point cloud registration method using a
pre-built HD map and integrated with GNSS observations in a mixed measurement
model to produce precise float solutions, which in turn increase the ambiguity
success rate. Closed-form expressions of the ambiguity variance matrix and the
associated Ambiguity Dilution of Precision (ADOP) are developed to provide a
priori evaluation of such lidar-aided ambiguity resolution performance. Both
analytical and experimental results show that the proposed method enables
successful instantaneous IAR with limited GNSS satellites and frequencies,
leading to centimeter-level vehicle positioning.
|
[
{
"version": "v1",
"created": "Tue, 26 Apr 2022 06:36:45 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Jun 2022 02:00:51 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Zhang",
"Junjie",
""
],
[
"Khodabandeh",
"Amir",
""
],
[
"Khoshelham",
"Kourosh",
""
]
] |
new_dataset
| 0.998437 |
2206.04382
|
Youwang Kim
|
Kim Youwang, Kim Ji-Yeon, Tae-Hyun Oh
|
CLIP-Actor: Text-Driven Recommendation and Stylization for Animating
Human Meshes
|
Accepted at ECCV 2022. [Project page] https://clip-actor.github.io
[Code] https://github.com/postech-ami/CLIP-Actor
| null | null | null |
cs.CV cs.AI cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose CLIP-Actor, a text-driven motion recommendation and neural mesh
stylization system for human mesh animation. CLIP-Actor animates a 3D human
mesh to conform to a text prompt by recommending a motion sequence and
optimizing mesh style attributes. We build a text-driven human motion
recommendation system by leveraging a large-scale human motion dataset with
language labels. Given a natural language prompt, CLIP-Actor suggests a
text-conforming human motion in a coarse-to-fine manner. Then, our novel
zero-shot neural style optimization detailizes and texturizes the recommended
mesh sequence to conform to the prompt in a temporally-consistent and
pose-agnostic manner. This is distinctive in that prior work fails to generate
plausible results when the pose of an artist-designed mesh does not conform to
the text from the beginning. We further propose the spatio-temporal view
augmentation and mask-weighted embedding attention, which stabilize the
optimization process by leveraging multi-frame human motion and rejecting
poorly rendered views. We demonstrate that CLIP-Actor produces plausible and
human-recognizable style 3D human mesh in motion with detailed geometry and
texture solely from a natural language prompt.
|
[
{
"version": "v1",
"created": "Thu, 9 Jun 2022 09:50:39 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 07:43:04 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Youwang",
"Kim",
""
],
[
"Ji-Yeon",
"Kim",
""
],
[
"Oh",
"Tae-Hyun",
""
]
] |
new_dataset
| 0.999727 |
2206.08194
|
Romain Loiseau
|
Romain Loiseau and Mathieu Aubry and Lo\"ic Landrieu
|
Online Segmentation of LiDAR Sequences: Dataset and Algorithm
|
Code and data are available at: https://romainloiseau.fr/helixnet
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Roof-mounted spinning LiDAR sensors are widely used by autonomous vehicles.
However, most semantic datasets and algorithms used for LiDAR sequence
segmentation operate on $360^\circ$ frames, causing an acquisition latency
incompatible with real-time applications. To address this issue, we first
introduce HelixNet, a $10$ billion point dataset with fine-grained labels,
timestamps, and sensor rotation information necessary to accurately assess the
real-time readiness of segmentation algorithms. Second, we propose Helix4D, a
compact and efficient spatio-temporal transformer architecture specifically
designed for rotating LiDAR sequences. Helix4D operates on acquisition slices
corresponding to a fraction of a full sensor rotation, significantly reducing
the total latency. Helix4D reaches accuracy on par with the best segmentation
algorithms on HelixNet and SemanticKITTI with a reduction of over $5\times$ in
terms of latency and $50\times$ in model size. The code and data are available
at: https://romainloiseau.fr/helixnet
|
[
{
"version": "v1",
"created": "Thu, 16 Jun 2022 14:08:58 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 08:40:56 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Loiseau",
"Romain",
""
],
[
"Aubry",
"Mathieu",
""
],
[
"Landrieu",
"Loïc",
""
]
] |
new_dataset
| 0.999858 |
2206.13179
|
Yiyang Hao
|
Yiyang Hao (1), Ge Li (2), Yongqiang Liu (1), Xiaowei Miao (1), He
Zong (1), Siyuan Jiang (1), Yang Liu (1), He Wei (1) ((1) aiXcoder, (2)
Peking University)
|
AixBench: A Code Generation Benchmark Dataset
| null | null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
We present a benchmark dataset for evaluating method-level code generation
task. The benchmark contains a dataset of 175 samples for automated evaluation
and a dataset of 161 samples for manual evaluation. We also present a new
metric for automatically evaluating the correctness of the generated code, and
a set of criteria to manually evaluating the overall quality of the generated
code.
|
[
{
"version": "v1",
"created": "Mon, 27 Jun 2022 10:44:48 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 02:55:15 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Hao",
"Yiyang",
""
],
[
"Li",
"Ge",
""
],
[
"Liu",
"Yongqiang",
""
],
[
"Miao",
"Xiaowei",
""
],
[
"Zong",
"He",
""
],
[
"Jiang",
"Siyuan",
""
],
[
"Liu",
"Yang",
""
],
[
"Wei",
"He",
""
]
] |
new_dataset
| 0.999837 |
2207.01700
|
Edward Kim
|
Edward Kim, Tobias Andersen, Marventus, A.E., Pedro Borges, David
Schmidt, Matthew Western
|
Emergency Management and Recovery of Luna Classic
|
22 pages
| null | null | null |
cs.CR cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
In early May 2022, the Terra ecosystem collapsed after the algorithmic
stablecoin failed to maintain its peg. Emergency measures were taken by
Terraform Labs (TFL) in an attempt to protect Luna and UST, but then were
abruptly abandoned by TFL for Luna 2.0 several days later. At this time, the
Luna Classic blockchain has been left crippled and in limbo for the last two
months.
In the face of impossible odds, the Luna Classic community has self organized
and rallied to build and restore the blockchain. This technical document
outlines the steps we, the community, have taken towards the emergency
management of the Luna Classic blockchain in the weeks after the UST depeg. We
outline precisely what would be implemented on-chain to mitigate the concerns
of affected stakeholders, and build trust for external partners, exchanges, and
third-party developers. For the Luna Classic community, validators, and
developers, this outlines concrete steps on how passed governance can and will
be achieved. We openly audit our own code and welcome any feedback for
improvement. Let us move forward together as the true community blockchain.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 19:54:59 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 06:36:21 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Kim",
"Edward",
""
],
[
"Andersen",
"Tobias",
""
],
[
"Marventus",
"",
""
],
[
"E.",
"A.",
""
],
[
"Borges",
"Pedro",
""
],
[
"Schmidt",
"David",
""
],
[
"Western",
"Matthew",
""
]
] |
new_dataset
| 0.988132 |
2207.05223
|
Shijie Chen
|
Shijie Chen, Ziru Chen, Xiang Deng, Ashley Lewis, Lingbo Mo, Samuel
Stevens, Zhen Wang, Xiang Yue, Tianshu Zhang, Yu Su, Huan Sun
|
Bootstrapping a User-Centered Task-Oriented Dialogue System
|
Published in 1st Proceedings of Alexa Prize TaskBot (Alexa Prize
2021). TacoBot won 3rd place in the challenge. See project website
https://sunlab-osu.github.io/tacobot/ for details
| null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We present TacoBot, a task-oriented dialogue system built for the inaugural
Alexa Prize TaskBot Challenge, which assists users in completing multi-step
cooking and home improvement tasks. TacoBot is designed with a user-centered
principle and aspires to deliver a collaborative and accessible dialogue
experience. Towards that end, it is equipped with accurate language
understanding, flexible dialogue management, and engaging response generation.
Furthermore, TacoBot is backed by a strong search engine and an automated
end-to-end test suite. In bootstrapping the development of TacoBot, we explore
a series of data augmentation strategies to train advanced neural language
processing models and continuously improve the dialogue experience with
collected real conversations. At the end of the semifinals, TacoBot achieved an
average rating of 3.55/5.0.
|
[
{
"version": "v1",
"created": "Mon, 11 Jul 2022 23:32:54 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 04:57:18 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Chen",
"Shijie",
""
],
[
"Chen",
"Ziru",
""
],
[
"Deng",
"Xiang",
""
],
[
"Lewis",
"Ashley",
""
],
[
"Mo",
"Lingbo",
""
],
[
"Stevens",
"Samuel",
""
],
[
"Wang",
"Zhen",
""
],
[
"Yue",
"Xiang",
""
],
[
"Zhang",
"Tianshu",
""
],
[
"Su",
"Yu",
""
],
[
"Sun",
"Huan",
""
]
] |
new_dataset
| 0.990429 |
2207.09812
|
Dawit Mureja Argaw
|
Dawit Mureja Argaw, Fabian Caba Heilbron, Joon-Young Lee, Markus
Woodson, In So Kweon
|
The Anatomy of Video Editing: A Dataset and Benchmark Suite for
AI-Assisted Video Editing
|
Code is available at: https://github.com/dawitmureja/AVE.git
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Machine learning is transforming the video editing industry. Recent advances
in computer vision have leveled-up video editing tasks such as intelligent
reframing, rotoscoping, color grading, or applying digital makeups. However,
most of the solutions have focused on video manipulation and VFX. This work
introduces the Anatomy of Video Editing, a dataset, and benchmark, to foster
research in AI-assisted video editing. Our benchmark suite focuses on video
editing tasks, beyond visual effects, such as automatic footage organization
and assisted video assembling. To enable research on these fronts, we annotate
more than 1.5M tags, with relevant concepts to cinematography, from 196176
shots sampled from movie scenes. We establish competitive baseline methods and
detailed analyses for each of the tasks. We hope our work sparks innovative
research towards underexplored areas of AI-assisted video editing.
|
[
{
"version": "v1",
"created": "Wed, 20 Jul 2022 10:53:48 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2022 06:53:02 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Argaw",
"Dawit Mureja",
""
],
[
"Heilbron",
"Fabian Caba",
""
],
[
"Lee",
"Joon-Young",
""
],
[
"Woodson",
"Markus",
""
],
[
"Kweon",
"In So",
""
]
] |
new_dataset
| 0.999802 |
2207.10143
|
Sarra Habchi
|
Sarra Habchi, Guillaume Haben, Jeongju Sohn, Adriano Franci, Mike
Papadakis, Maxime Cordy, Yves Le Traon
|
What Made This Test Flake? Pinpointing Classes Responsible for Test
Flakiness
|
Accepted at the 38th IEEE International Conference on Software
Maintenance and Evolution (ICSME)
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Flaky tests are defined as tests that manifest non-deterministic behaviour by
passing and failing intermittently for the same version of the code. These
tests cripple continuous integration with false alerts that waste developers'
time and break their trust in regression testing. To mitigate the effects of
flakiness, both researchers and industrial experts proposed strategies and
tools to detect and isolate flaky tests. However, flaky tests are rarely fixed
as developers struggle to localise and understand their causes. Additionally,
developers working with large codebases often need to know the sources of
non-determinism to preserve code quality, i.e., avoid introducing technical
debt linked with non-deterministic behaviour, and to avoid introducing new
flaky tests. To aid with these tasks, we propose re-targeting Fault
Localisation techniques to the flaky component localisation problem, i.e.,
pinpointing program classes that cause the non-deterministic behaviour of flaky
tests. In particular, we employ Spectrum-Based Fault Localisation (SBFL), a
coverage-based fault localisation technique commonly adopted for its simplicity
and effectiveness. We also utilise other data sources, such as change history
and static code metrics, to further improve the localisation. Our results show
that augmenting SBFL with change and code metrics ranks flaky classes in the
top-1 and top-5 suggestions, in 26% and 47% of the cases. Overall, we
successfully reduced the average number of classes inspected to locate the
first flaky class to 19% of the total number of classes covered by flaky tests.
Our results also show that localisation methods are effective in major
flakiness categories, such as concurrency and asynchronous waits, indicating
their general ability to identify flaky components.
|
[
{
"version": "v1",
"created": "Wed, 20 Jul 2022 18:46:22 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Habchi",
"Sarra",
""
],
[
"Haben",
"Guillaume",
""
],
[
"Sohn",
"Jeongju",
""
],
[
"Franci",
"Adriano",
""
],
[
"Papadakis",
"Mike",
""
],
[
"Cordy",
"Maxime",
""
],
[
"Traon",
"Yves Le",
""
]
] |
new_dataset
| 0.996508 |
2207.10225
|
Elijah Cole
|
Elijah Cole, Kimberly Wilber, Grant Van Horn, Xuan Yang, Marco
Fornoni, Pietro Perona, Serge Belongie, Andrew Howard, Oisin Mac Aodha
|
On Label Granularity and Object Localization
|
ECCV 2022
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Weakly supervised object localization (WSOL) aims to learn representations
that encode object location using only image-level category labels. However,
many objects can be labeled at different levels of granularity. Is it an
animal, a bird, or a great horned owl? Which image-level labels should we use?
In this paper we study the role of label granularity in WSOL. To facilitate
this investigation we introduce iNatLoc500, a new large-scale fine-grained
benchmark dataset for WSOL. Surprisingly, we find that choosing the right
training label granularity provides a much larger performance boost than
choosing the best WSOL algorithm. We also show that changing the label
granularity can significantly improve data efficiency.
|
[
{
"version": "v1",
"created": "Wed, 20 Jul 2022 22:51:32 GMT"
}
] | 2022-07-22T00:00:00 |
[
[
"Cole",
"Elijah",
""
],
[
"Wilber",
"Kimberly",
""
],
[
"Van Horn",
"Grant",
""
],
[
"Yang",
"Xuan",
""
],
[
"Fornoni",
"Marco",
""
],
[
"Perona",
"Pietro",
""
],
[
"Belongie",
"Serge",
""
],
[
"Howard",
"Andrew",
""
],
[
"Mac Aodha",
"Oisin",
""
]
] |
new_dataset
| 0.999461 |
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