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values | categories stringlengths 5 108 | license stringclasses 9
values | abstract stringlengths 83 3.41k | versions listlengths 1 20 | update_date timestamp[s]date 2007-05-23 00:00:00 2025-04-11 00:00:00 | authors_parsed listlengths 1 427 | prompt stringlengths 166 3.49k | label stringclasses 2
values | prob float64 0.5 0.98 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2204.07661 | Soumyajit Gupta | Soumyajit Gupta, Venelin Kovatchev, Anubrata Das, Maria De-Arteaga,
Matthew Lease | Finding Pareto Trade-offs in Fair and Accurate Detection of Toxic Speech | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Optimizing NLP models for fairness poses many challenges. Lack of
differentiable fairness measures prevents gradient-based loss training or
requires surrogate losses that diverge from the true metric of interest. In
addition, competing objectives (e.g., accuracy vs. fairness) often require
making trade-offs based on ... | [
{
"version": "v1",
"created": "Fri, 15 Apr 2022 22:11:25 GMT"
},
{
"version": "v2",
"created": "Tue, 10 May 2022 18:36:41 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 00:29:44 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Gupta",
"Soumyajit",
""
],
[
"Kovatchev",
"Venelin",
""
],
[
"Das",
"Anubrata",
""
],
[
"De-Arteaga",
"Maria",
""
],
[
"Lease",
"Matthew",
""
]
] | TITLE: Finding Pareto Trade-offs in Fair and Accurate Detection of Toxic Speech
ABSTRACT: Optimizing NLP models for fairness poses many challenges. Lack of
differentiable fairness measures prevents gradient-based loss training or
requires surrogate losses that diverge from the true metric of interest. In
addition, ... | no_new_dataset | 0.947235 |
2303.17408 | Yucheng Ruan | Yucheng Ruan, Xiang Lan, Daniel J. Tan, Hairil Rizal Abdullah,
Mengling Feng | P-Transformer: A Prompt-based Multimodal Transformer Architecture For
Medical Tabular Data | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Medical tabular data, abundant in Electronic Health Records (EHRs), is a
valuable resource for diverse medical tasks such as risk prediction. While deep
learning approaches, particularly transformer-based models, have shown
remarkable performance in tabular data prediction, there are still problems
remaining for exis... | [
{
"version": "v1",
"created": "Thu, 30 Mar 2023 14:25:44 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Aug 2023 08:58:25 GMT"
},
{
"version": "v3",
"created": "Tue, 9 Jan 2024 10:28:00 GMT"
},
{
"version": "v4",
"created": "Thu, 10 Apr 2025 06:24:36 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Ruan",
"Yucheng",
""
],
[
"Lan",
"Xiang",
""
],
[
"Tan",
"Daniel J.",
""
],
[
"Abdullah",
"Hairil Rizal",
""
],
[
"Feng",
"Mengling",
""
]
] | TITLE: P-Transformer: A Prompt-based Multimodal Transformer Architecture For
Medical Tabular Data
ABSTRACT: Medical tabular data, abundant in Electronic Health Records (EHRs), is a
valuable resource for diverse medical tasks such as risk prediction. While deep
learning approaches, particularly transformer-based m... | no_new_dataset | 0.944638 |
2305.03535 | Jonas Hein | Jonas Hein, Nicola Cavalcanti, Daniel Suter, Lukas Zingg, Fabio
Carrillo, Lilian Calvet, Mazda Farshad, Marc Pollefeys, Nassir Navab, Philipp
F\"urnstahl | Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose
Estimation of Surgical Instruments | Accepted for publication in Medical Image Analysis. Project page:
https://jonashein.github.io/mvpsp/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | State-of-the-art research of traditional computer vision is increasingly
leveraged in the surgical domain. A particular focus in computer-assisted
surgery is to replace marker-based tracking systems for instrument localization
with pure image-based 6DoF pose estimation using deep-learning methods.
However, state-of-t... | [
{
"version": "v1",
"created": "Fri, 5 May 2023 13:42:19 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Dec 2023 20:52:50 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 17:23:33 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Hein",
"Jonas",
""
],
[
"Cavalcanti",
"Nicola",
""
],
[
"Suter",
"Daniel",
""
],
[
"Zingg",
"Lukas",
""
],
[
"Carrillo",
"Fabio",
""
],
[
"Calvet",
"Lilian",
""
],
[
"Farshad",
"Mazda",
""
],
[
"Po... | TITLE: Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose
Estimation of Surgical Instruments
ABSTRACT: State-of-the-art research of traditional computer vision is increasingly
leveraged in the surgical domain. A particular focus in computer-assisted
surgery is to replace marker-based tracking s... | new_dataset | 0.963161 |
2305.15932 | Jie He | Jie He and Simon Chi Lok U and V\'ictor Guti\'errez-Basulto and Jeff
Z. Pan | BUCA: A Binary Classification Approach to Unsupervised Commonsense
Question Answering | There is a text error in Table 10 | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as
the construction of commonsense reasoning datasets is expensive, and they are
inevitably limited in their scope. A popular approach to UCR is to fine-tune
language models with external knowledge (e.g., knowledge graphs), but this
usually req... | [
{
"version": "v1",
"created": "Thu, 25 May 2023 10:59:47 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Jun 2023 20:33:09 GMT"
},
{
"version": "v3",
"created": "Mon, 10 Mar 2025 09:28:29 GMT"
},
{
"version": "v4",
"created": "Wed, 9 Apr 2025 21:40:10 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"He",
"Jie",
""
],
[
"U",
"Simon Chi Lok",
""
],
[
"Gutiérrez-Basulto",
"Víctor",
""
],
[
"Pan",
"Jeff Z.",
""
]
] | TITLE: BUCA: A Binary Classification Approach to Unsupervised Commonsense
Question Answering
ABSTRACT: Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as
the construction of commonsense reasoning datasets is expensive, and they are
inevitably limited in their scope. A popular approach to... | no_new_dataset | 0.947672 |
2308.07223 | Melanie Roschewitz | M\'elanie Roschewitz and Ben Glocker | Distance Matters For Improving Performance Estimation Under Covariate
Shift | Accepted to ICCV Workshop on Uncertainty Quantification for Computer
Vision 2023 | Proceedings of the IEEE/CVF International Conference on Computer
Vision (ICCV) Workshops, 2023, pp. 4549-4559 | 10.1109/ICCVW60793.2023.00489 | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Performance estimation under covariate shift is a crucial component of safe
AI model deployment, especially for sensitive use-cases. Recently, several
solutions were proposed to tackle this problem, most leveraging model
predictions or softmax confidence to derive accuracy estimates. However, under
dataset shifts, co... | [
{
"version": "v1",
"created": "Mon, 14 Aug 2023 15:49:19 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Roschewitz",
"Mélanie",
""
],
[
"Glocker",
"Ben",
""
]
] | TITLE: Distance Matters For Improving Performance Estimation Under Covariate
Shift
ABSTRACT: Performance estimation under covariate shift is a crucial component of safe
AI model deployment, especially for sensitive use-cases. Recently, several
solutions were proposed to tackle this problem, most leveraging model
... | no_new_dataset | 0.946349 |
2309.15408 | Mario Beraha | Mario Beraha, Stefano Favaro, Matteo Sesia | A smoothed-Bayesian approach to frequency recovery from sketched data | null | null | null | null | stat.ME cs.DS cs.IR math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We provide a novel statistical perspective on a classical problem at the
intersection of computer science and information theory: recovering the
empirical frequency of a symbol in a large discrete dataset using only a
compressed representation, or sketch, obtained via random hashing. Departing
from traditional algori... | [
{
"version": "v1",
"created": "Wed, 27 Sep 2023 05:20:53 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jun 2024 13:15:11 GMT"
},
{
"version": "v3",
"created": "Thu, 28 Nov 2024 13:46:38 GMT"
},
{
"version": "v4",
"created": "Thu, 10 Apr 2025 08:21:29 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Beraha",
"Mario",
""
],
[
"Favaro",
"Stefano",
""
],
[
"Sesia",
"Matteo",
""
]
] | TITLE: A smoothed-Bayesian approach to frequency recovery from sketched data
ABSTRACT: We provide a novel statistical perspective on a classical problem at the
intersection of computer science and information theory: recovering the
empirical frequency of a symbol in a large discrete dataset using only a
compressed ... | no_new_dataset | 0.9434 |
2311.06293 | Zeynab Kaseb | Zeynab Kaseb, Matthias Moller, Giorgio Tosti Balducci, Peter Palensky,
Pedro P. Vergara | Quantum Neural Networks for Power Flow Analysis | 8 pages, 13 figures | null | 10.1016/j.epsr.2024.110677 | null | quant-ph cs.LG cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | This paper explores the potential application of quantum and hybrid
quantum-classical neural networks in power flow analysis. Experiments are
conducted using two datasets based on 4-bus and 33-bus test systems. A
systematic performance comparison is also conducted among quantum, hybrid
quantum-classical, and classica... | [
{
"version": "v1",
"created": "Sat, 4 Nov 2023 11:25:31 GMT"
},
{
"version": "v2",
"created": "Sun, 10 Mar 2024 15:49:57 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Kaseb",
"Zeynab",
""
],
[
"Moller",
"Matthias",
""
],
[
"Balducci",
"Giorgio Tosti",
""
],
[
"Palensky",
"Peter",
""
],
[
"Vergara",
"Pedro P.",
""
]
] | TITLE: Quantum Neural Networks for Power Flow Analysis
ABSTRACT: This paper explores the potential application of quantum and hybrid
quantum-classical neural networks in power flow analysis. Experiments are
conducted using two datasets based on 4-bus and 33-bus test systems. A
systematic performance comparison is a... | no_new_dataset | 0.951278 |
2311.13706 | Nicol\'as Gaggion Ph.D. | Nicol\'as Gaggion, Benjamin A. Matheson, Yan Xia, Rodrigo Bonazzola,
Nishant Ravikumar, Zeike A. Taylor, Diego H. Milone, Alejandro F. Frangi,
Enzo Ferrante | Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh
Reconstruction in Cardiovascular MRI | null | null | null | null | eess.IV cs.CV | http://creativecommons.org/licenses/by-sa/4.0/ | Cardiovascular magnetic resonance imaging is emerging as a crucial tool to
examine cardiac morphology and function. Essential to this endeavour are
anatomical 3D surface and volumetric meshes derived from CMR images, which
facilitate computational anatomy studies, biomarker discovery, and in-silico
simulations. Tradi... | [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 21:51:29 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Aug 2024 19:18:41 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 16:25:45 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Gaggion",
"Nicolás",
""
],
[
"Matheson",
"Benjamin A.",
""
],
[
"Xia",
"Yan",
""
],
[
"Bonazzola",
"Rodrigo",
""
],
[
"Ravikumar",
"Nishant",
""
],
[
"Taylor",
"Zeike A.",
""
],
[
"Milone",
"Diego H.",
""
... | TITLE: Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh
Reconstruction in Cardiovascular MRI
ABSTRACT: Cardiovascular magnetic resonance imaging is emerging as a crucial tool to
examine cardiac morphology and function. Essential to this endeavour are
anatomical 3D surface and volumetric meshes der... | no_new_dataset | 0.955194 |
2401.03048 | Xin Ma | Xin Ma, Yaohui Wang, Xinyuan Chen, Gengyun Jia, Ziwei Liu, Yuan-Fang
Li, Cunjian Chen, Yu Qiao | Latte: Latent Diffusion Transformer for Video Generation | Accepted by Transactions on Machine Learning Research 2025; Project
page: https://maxin-cn.github.io/latte_project | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel Latent Diffusion Transformer, namely Latte, for video
generation. Latte first extracts spatio-temporal tokens from input videos and
then adopts a series of Transformer blocks to model video distribution in the
latent space. In order to model a substantial number of tokens extracted from
videos, fou... | [
{
"version": "v1",
"created": "Fri, 5 Jan 2024 19:55:15 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 09:28:20 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Ma",
"Xin",
""
],
[
"Wang",
"Yaohui",
""
],
[
"Chen",
"Xinyuan",
""
],
[
"Jia",
"Gengyun",
""
],
[
"Liu",
"Ziwei",
""
],
[
"Li",
"Yuan-Fang",
""
],
[
"Chen",
"Cunjian",
""
],
[
"Qiao",
"Yu",
... | TITLE: Latte: Latent Diffusion Transformer for Video Generation
ABSTRACT: We propose a novel Latent Diffusion Transformer, namely Latte, for video
generation. Latte first extracts spatio-temporal tokens from input videos and
then adopts a series of Transformer blocks to model video distribution in the
latent space.... | no_new_dataset | 0.949106 |
2402.18206 | Abhijnya Bhat | Rishubh Parihar, Abhijnya Bhat, Abhipsa Basu, Saswat Mallick, Jogendra
Nath Kundu, R. Venkatesh Babu | Balancing Act: Distribution-Guided Debiasing in Diffusion Models | CVPR 2024. Project Page : https://ab-34.github.io/balancing_act/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Diffusion Models (DMs) have emerged as powerful generative models with
unprecedented image generation capability. These models are widely used for
data augmentation and creative applications. However, DMs reflect the biases
present in the training datasets. This is especially concerning in the context
of faces, where... | [
{
"version": "v1",
"created": "Wed, 28 Feb 2024 09:53:17 GMT"
},
{
"version": "v2",
"created": "Wed, 22 May 2024 17:23:22 GMT"
},
{
"version": "v3",
"created": "Wed, 29 May 2024 13:33:57 GMT"
},
{
"version": "v4",
"created": "Thu, 10 Apr 2025 14:39:59 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Parihar",
"Rishubh",
""
],
[
"Bhat",
"Abhijnya",
""
],
[
"Basu",
"Abhipsa",
""
],
[
"Mallick",
"Saswat",
""
],
[
"Kundu",
"Jogendra Nath",
""
],
[
"Babu",
"R. Venkatesh",
""
]
] | TITLE: Balancing Act: Distribution-Guided Debiasing in Diffusion Models
ABSTRACT: Diffusion Models (DMs) have emerged as powerful generative models with
unprecedented image generation capability. These models are widely used for
data augmentation and creative applications. However, DMs reflect the biases
present in... | no_new_dataset | 0.951684 |
2402.18307 | Joanne Lin | Joanne Lin, Nantheera Anantrasirichai, David Bull | Multi-Scale Denoising in the Feature Space for Low-Light Instance
Segmentation | Accepted by ICASSP 2025 | 2025 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), Hyderabad, India, 2025, pp. 1-5 | 10.1109/ICASSP49660.2025.10889336 | ICASSP 2025 | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Instance segmentation for low-light imagery remains largely unexplored due to
the challenges imposed by such conditions, for example shot noise due to low
photon count, color distortions and reduced contrast. In this paper, we propose
an end-to-end solution to address this challenging task. Our proposed method
implem... | [
{
"version": "v1",
"created": "Wed, 28 Feb 2024 13:07:16 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Sep 2024 08:50:11 GMT"
},
{
"version": "v3",
"created": "Thu, 2 Jan 2025 23:06:37 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Lin",
"Joanne",
""
],
[
"Anantrasirichai",
"Nantheera",
""
],
[
"Bull",
"David",
""
]
] | TITLE: Multi-Scale Denoising in the Feature Space for Low-Light Instance
Segmentation
ABSTRACT: Instance segmentation for low-light imagery remains largely unexplored due to
the challenges imposed by such conditions, for example shot noise due to low
photon count, color distortions and reduced contrast. In this p... | no_new_dataset | 0.949106 |
2404.05297 | Sujin Han | Sujin Han, Jinseo Kim, Sung-Ju Lee, Insu Yun | Automated Attack Synthesis for Constant Product Market Makers | 22 pages, 16 figures, 8 tables. Accepted at ACM ISSTA 2025 | null | 10.1145/3728872 | null | cs.CR cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decentralized Finance (DeFi) enables many novel applications that were
impossible in traditional finances. However, it also introduces new types of
vulnerabilities. An example of such vulnerabilities is a composability bug
between token contracts and Decentralized Exchange (DEX) that follows the
Constant Product Mark... | [
{
"version": "v1",
"created": "Mon, 8 Apr 2024 08:35:15 GMT"
},
{
"version": "v2",
"created": "Thu, 25 Apr 2024 01:02:53 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 06:19:13 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Han",
"Sujin",
""
],
[
"Kim",
"Jinseo",
""
],
[
"Lee",
"Sung-Ju",
""
],
[
"Yun",
"Insu",
""
]
] | TITLE: Automated Attack Synthesis for Constant Product Market Makers
ABSTRACT: Decentralized Finance (DeFi) enables many novel applications that were
impossible in traditional finances. However, it also introduces new types of
vulnerabilities. An example of such vulnerabilities is a composability bug
between token ... | no_new_dataset | 0.934783 |
2404.10702 | Arka Ujjal Dey | Arka Ujjal Dey, Artemis Llabr\'es, Ernest Valveny and Dimosthenis
Karatzas | Retrieval Augmented Verification for Zero-Shot Detection of Multimodal
Disinformation | null | null | null | null | cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rise of disinformation on social media, especially through the strategic
manipulation or repurposing of images, paired with provocative text, presents a
complex challenge for traditional fact-checking methods. In this paper, we
introduce a novel zero-shot approach to identify and interpret such multimodal
disinfo... | [
{
"version": "v1",
"created": "Tue, 16 Apr 2024 16:19:22 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Apr 2024 17:19:53 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 22:23:08 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Dey",
"Arka Ujjal",
""
],
[
"Llabrés",
"Artemis",
""
],
[
"Valveny",
"Ernest",
""
],
[
"Karatzas",
"Dimosthenis",
""
]
] | TITLE: Retrieval Augmented Verification for Zero-Shot Detection of Multimodal
Disinformation
ABSTRACT: The rise of disinformation on social media, especially through the strategic
manipulation or repurposing of images, paired with provocative text, presents a
complex challenge for traditional fact-checking method... | no_new_dataset | 0.9463 |
2405.16924 | Francesco Montagna | Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar, Francesco
Locatello | Demystifying amortized causal discovery with transformers | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Supervised learning approaches for causal discovery from observational data
often achieve competitive performance despite seemingly avoiding explicit
assumptions that traditional methods make for identifiability. In this work, we
investigate CSIvA (Ke et al., 2023), a transformer-based model promising to
train on syn... | [
{
"version": "v1",
"created": "Mon, 27 May 2024 08:17:49 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 20:30:46 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Montagna",
"Francesco",
""
],
[
"Cairney-Leeming",
"Max",
""
],
[
"Sridhar",
"Dhanya",
""
],
[
"Locatello",
"Francesco",
""
]
] | TITLE: Demystifying amortized causal discovery with transformers
ABSTRACT: Supervised learning approaches for causal discovery from observational data
often achieve competitive performance despite seemingly avoiding explicit
assumptions that traditional methods make for identifiability. In this work, we
investigate... | no_new_dataset | 0.943138 |
2405.18560 | Shubhang Bhatnagar | Shubhang Bhatnagar, Narendra Ahuja | Potential Field Based Deep Metric Learning | Accepted to CVPR 2025 | null | null | null | cs.CV cs.AI cs.IR cs.LG eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep metric learning (DML) involves training a network to learn a
semantically meaningful representation space. Many current approaches mine
n-tuples of examples and model interactions within each tuplets. We present a
novel, compositional DML model that instead of in tuples, represents the
influence of each example ... | [
{
"version": "v1",
"created": "Tue, 28 May 2024 20:10:06 GMT"
},
{
"version": "v2",
"created": "Sun, 1 Dec 2024 05:22:22 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 04:49:39 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Bhatnagar",
"Shubhang",
""
],
[
"Ahuja",
"Narendra",
""
]
] | TITLE: Potential Field Based Deep Metric Learning
ABSTRACT: Deep metric learning (DML) involves training a network to learn a
semantically meaningful representation space. Many current approaches mine
n-tuples of examples and model interactions within each tuplets. We present a
novel, compositional DML model that i... | no_new_dataset | 0.945851 |
2406.07409 | Juntao You | HanQin Cai, Longxiu Huang, Xiliang Lu, Juntao You | Accelerating Ill-conditioned Hankel Matrix Recovery via Structured
Newton-like Descent | null | null | null | null | stat.ML cs.IT cs.LG eess.SP math.IT math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies the robust Hankel recovery problem, which simultaneously
removes the sparse outliers and fulfills missing entries from the partial
observation. We propose a novel non-convex algorithm, coined Hankel Structured
Newton-Like Descent (HSNLD), to tackle the robust Hankel recovery problem.
HSNLD is highl... | [
{
"version": "v1",
"created": "Tue, 11 Jun 2024 16:14:30 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 07:55:52 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Cai",
"HanQin",
""
],
[
"Huang",
"Longxiu",
""
],
[
"Lu",
"Xiliang",
""
],
[
"You",
"Juntao",
""
]
] | TITLE: Accelerating Ill-conditioned Hankel Matrix Recovery via Structured
Newton-like Descent
ABSTRACT: This paper studies the robust Hankel recovery problem, which simultaneously
removes the sparse outliers and fulfills missing entries from the partial
observation. We propose a novel non-convex algorithm, coined... | no_new_dataset | 0.946051 |
2406.12123 | Jingxi Xu | Jingxi Xu, Runsheng Wang, Siqi Shang, Ava Chen, Lauren Winterbottom,
To-Liang Hsu, Wenxi Chen, Khondoker Ahmed, Pedro Leandro La Rotta, Xinyue
Zhu, Dawn M. Nilsen, Joel Stein, Matei Ciocarlie | ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis
for Stroke | 8 pages; accepted to RA-L in November 2024; published at RA-L in
February 2025 | null | null | null | cs.RO cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intent inferral on a hand orthosis for stroke patients is challenging due to
the difficulty of data collection. Additionally, EMG signals exhibit
significant variations across different conditions, sessions, and subjects,
making it hard for classifiers to generalize. Traditional approaches require a
large labeled dat... | [
{
"version": "v1",
"created": "Mon, 17 Jun 2024 22:04:44 GMT"
},
{
"version": "v2",
"created": "Fri, 22 Nov 2024 23:15:26 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 21:49:04 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Xu",
"Jingxi",
""
],
[
"Wang",
"Runsheng",
""
],
[
"Shang",
"Siqi",
""
],
[
"Chen",
"Ava",
""
],
[
"Winterbottom",
"Lauren",
""
],
[
"Hsu",
"To-Liang",
""
],
[
"Chen",
"Wenxi",
""
],
[
"Ahmed",
... | TITLE: ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis
for Stroke
ABSTRACT: Intent inferral on a hand orthosis for stroke patients is challenging due to
the difficulty of data collection. Additionally, EMG signals exhibit
significant variations across different conditions, sessions, and subj... | no_new_dataset | 0.948822 |
2406.19388 | Moayed Haji-Ali | Moayed Haji-Ali, Willi Menapace, Aliaksandr Siarohin, Guha
Balakrishnan, Sergey Tulyakov, Vicente Ordonez | Taming Data and Transformers for Scalable Audio Generation | Project Webpage: https://snap-research.github.io/GenAU/ | null | null | null | cs.SD cs.CL cs.CV cs.MM eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The scalability of ambient sound generators is hindered by data scarcity,
insufficient caption quality, and limited scalability in model architecture.
This work addresses these challenges by advancing both data and model scaling.
First, we propose an efficient and scalable dataset collection pipeline
tailored for amb... | [
{
"version": "v1",
"created": "Thu, 27 Jun 2024 17:58:54 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Oct 2024 17:56:21 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 17:55:02 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Haji-Ali",
"Moayed",
""
],
[
"Menapace",
"Willi",
""
],
[
"Siarohin",
"Aliaksandr",
""
],
[
"Balakrishnan",
"Guha",
""
],
[
"Tulyakov",
"Sergey",
""
],
[
"Ordonez",
"Vicente",
""
]
] | TITLE: Taming Data and Transformers for Scalable Audio Generation
ABSTRACT: The scalability of ambient sound generators is hindered by data scarcity,
insufficient caption quality, and limited scalability in model architecture.
This work addresses these challenges by advancing both data and model scaling.
First, we ... | no_new_dataset | 0.843895 |
2407.15817 | Baudouin Denis de Senneville PhD | Florian Robert, Alexia Calovoulos, Laurent Facq, Fanny Decoeur,
Etienne Gontier, Christophe F. Grosset, Baudouin Denis de Senneville | Enhancing Cell Instance Segmentation in Scanning Electron Microscopy
Images via a Deep Contour Closing Operator | 13 pages, 8 figures, 2 tables | null | 10.1016/j.compbiomed.2025.109972 | null | eess.IV cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurately segmenting and individualizing cells in SEM images is a highly
promising technique for elucidating tissue architecture in oncology. While
current AI-based methods are effective, errors persist, necessitating
time-consuming manual corrections, particularly in areas where the quality of
cell contours in the ... | [
{
"version": "v1",
"created": "Mon, 22 Jul 2024 17:32:06 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 09:20:30 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Robert",
"Florian",
""
],
[
"Calovoulos",
"Alexia",
""
],
[
"Facq",
"Laurent",
""
],
[
"Decoeur",
"Fanny",
""
],
[
"Gontier",
"Etienne",
""
],
[
"Grosset",
"Christophe F.",
""
],
[
"de Senneville",
"Baudouin D... | TITLE: Enhancing Cell Instance Segmentation in Scanning Electron Microscopy
Images via a Deep Contour Closing Operator
ABSTRACT: Accurately segmenting and individualizing cells in SEM images is a highly
promising technique for elucidating tissue architecture in oncology. While
current AI-based methods are effecti... | no_new_dataset | 0.959875 |
2409.10365 | Melanie Roschewitz | M\'elanie Roschewitz, Fabio De Sousa Ribeiro, Tian Xia, Galvin Khara,
Ben Glocker | Robust image representations with counterfactual contrastive learning | Code available at
https://github.com/biomedia-mira/counterfactual-contrastive/ | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Contrastive pretraining can substantially increase model generalisation and
downstream performance. However, the quality of the learned representations is
highly dependent on the data augmentation strategy applied to generate positive
pairs. Positive contrastive pairs should preserve semantic meaning while
discarding... | [
{
"version": "v1",
"created": "Mon, 16 Sep 2024 15:11:00 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 16:19:20 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Roschewitz",
"Mélanie",
""
],
[
"Ribeiro",
"Fabio De Sousa",
""
],
[
"Xia",
"Tian",
""
],
[
"Khara",
"Galvin",
""
],
[
"Glocker",
"Ben",
""
]
] | TITLE: Robust image representations with counterfactual contrastive learning
ABSTRACT: Contrastive pretraining can substantially increase model generalisation and
downstream performance. However, the quality of the learned representations is
highly dependent on the data augmentation strategy applied to generate pos... | no_new_dataset | 0.949529 |
2409.19075 | Jie He | Yu Fu, Jie He, Yifan Yang, Qun Liu, Deyi Xiong | Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource
Commonsense Reasoning | There is a text error in table 6 | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Meta learning has been widely used to exploit rich-resource source tasks to
improve the performance of low-resource target tasks. Unfortunately, most
existing meta learning approaches treat different source tasks equally,
ignoring the relatedness of source tasks to the target task in knowledge
transfer. To mitigate t... | [
{
"version": "v1",
"created": "Fri, 27 Sep 2024 18:22:22 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Mar 2025 09:31:15 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 21:49:23 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Fu",
"Yu",
""
],
[
"He",
"Jie",
""
],
[
"Yang",
"Yifan",
""
],
[
"Liu",
"Qun",
""
],
[
"Xiong",
"Deyi",
""
]
] | TITLE: Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource
Commonsense Reasoning
ABSTRACT: Meta learning has been widely used to exploit rich-resource source tasks to
improve the performance of low-resource target tasks. Unfortunately, most
existing meta learning approaches treat different sourc... | no_new_dataset | 0.944689 |
2410.01595 | Amin Karimi Monsefi | Pouyan Navard, Amin Karimi Monsefi, Mengxi Zhou, Wei-Lun Chao, Alper
Yilmaz, Rajiv Ramnath | KnobGen: Controlling the Sophistication of Artwork in Sketch-Based
Diffusion Models | Accepted to CVPR 2025 Workshop on CVEU | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent advances in diffusion models have significantly improved text-to-image
(T2I) generation, but they often struggle to balance fine-grained precision
with high-level control. Methods like ControlNet and T2I-Adapter excel at
following sketches by seasoned artists but tend to be overly rigid, replicating
unintentio... | [
{
"version": "v1",
"created": "Wed, 2 Oct 2024 14:33:12 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Oct 2024 12:47:48 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 22:27:10 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Navard",
"Pouyan",
""
],
[
"Monsefi",
"Amin Karimi",
""
],
[
"Zhou",
"Mengxi",
""
],
[
"Chao",
"Wei-Lun",
""
],
[
"Yilmaz",
"Alper",
""
],
[
"Ramnath",
"Rajiv",
""
]
] | TITLE: KnobGen: Controlling the Sophistication of Artwork in Sketch-Based
Diffusion Models
ABSTRACT: Recent advances in diffusion models have significantly improved text-to-image
(T2I) generation, but they often struggle to balance fine-grained precision
with high-level control. Methods like ControlNet and T2I-Ad... | new_dataset | 0.967564 |
2410.03749 | Larry Liebovitch | K. Lian (1), L. S. Liebovitch (1), M. Wild (1), H. West (1), P. T.
Coleman (1), F. Chen (2), E. Kimani (2), K. Sieck (2) ((1) Columbia
University, (2) Toyota Research Institute) | Machine Learning Classification of Peaceful Countries: A Comparative
Analysis and Dataset Optimization | 5 pages, 5 figures | 2025 59th Annual Conference on Information Sciences and Systems
(CISS), Baltimore, MD, USA, 2025, pp. 1-5 | 10.1109/CISS64860.2025.10944706 | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper presents a machine learning approach to classify countries as
peaceful or non-peaceful using linguistic patterns extracted from global media
articles. We employ vector embeddings and cosine similarity to develop a
supervised classification model that effectively identifies peaceful countries.
Additionally,... | [
{
"version": "v1",
"created": "Tue, 1 Oct 2024 19:28:03 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Lian",
"K.",
""
],
[
"Liebovitch",
"L. S.",
""
],
[
"Wild",
"M.",
""
],
[
"West",
"H.",
""
],
[
"Coleman",
"P. T.",
""
],
[
"Chen",
"F.",
""
],
[
"Kimani",
"E.",
""
],
[
"Sieck",
"K.",
""
... | TITLE: Machine Learning Classification of Peaceful Countries: A Comparative
Analysis and Dataset Optimization
ABSTRACT: This paper presents a machine learning approach to classify countries as
peaceful or non-peaceful using linguistic patterns extracted from global media
articles. We employ vector embeddings and ... | no_new_dataset | 0.950869 |
2410.08206 | Idil Esen Zulfikar | Ilya Fradlin, Idil Esen Zulfikar, Kadir Yilmaz, Theodora Kontogianni,
Bastian Leibe | Interactive4D: Interactive 4D LiDAR Segmentation | Accepted to ICRA2025! | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interactive segmentation has an important role in facilitating the annotation
process of future LiDAR datasets. Existing approaches sequentially segment
individual objects at each LiDAR scan, repeating the process throughout the
entire sequence, which is redundant and ineffective. In this work, we propose
interactive... | [
{
"version": "v1",
"created": "Thu, 10 Oct 2024 17:59:45 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 17:59:53 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Fradlin",
"Ilya",
""
],
[
"Zulfikar",
"Idil Esen",
""
],
[
"Yilmaz",
"Kadir",
""
],
[
"Kontogianni",
"Theodora",
""
],
[
"Leibe",
"Bastian",
""
]
] | TITLE: Interactive4D: Interactive 4D LiDAR Segmentation
ABSTRACT: Interactive segmentation has an important role in facilitating the annotation
process of future LiDAR datasets. Existing approaches sequentially segment
individual objects at each LiDAR scan, repeating the process throughout the
entire sequence, whic... | no_new_dataset | 0.953622 |
2410.13453 | Ant Duru | Ant Duru, Alptekin Temizel | Adaptive Augmentation Policy Optimization with LLM Feedback | 15 pages, 4 tables, 3 figures submitted for consideration to 2025
Medical Image Understanding and Analysis Conference (MIUA) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data augmentation is a critical component of deep learning pipelines,
enhancing model generalization by increasing dataset diversity. Traditional
augmentation strategies rely on manually designed transformations, stochastic
sampling, or automated search-based approaches. Although automated methods
improve performance... | [
{
"version": "v1",
"created": "Thu, 17 Oct 2024 11:26:10 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 11:05:01 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 18:00:00 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Duru",
"Ant",
""
],
[
"Temizel",
"Alptekin",
""
]
] | TITLE: Adaptive Augmentation Policy Optimization with LLM Feedback
ABSTRACT: Data augmentation is a critical component of deep learning pipelines,
enhancing model generalization by increasing dataset diversity. Traditional
augmentation strategies rely on manually designed transformations, stochastic
sampling, or au... | no_new_dataset | 0.949295 |
2410.22318 | Can Chen | Can Chen, Jun-Kun Wang | Online Detecting LLM-Generated Texts via Sequential Hypothesis Testing
by Betting | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Developing algorithms to differentiate between machine-generated texts and
human-written texts has garnered substantial attention in recent years.
Existing methods in this direction typically concern an offline setting where a
dataset containing a mix of real and machine-generated texts is given upfront,
and the task... | [
{
"version": "v1",
"created": "Tue, 29 Oct 2024 17:55:14 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 00:51:20 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Chen",
"Can",
""
],
[
"Wang",
"Jun-Kun",
""
]
] | TITLE: Online Detecting LLM-Generated Texts via Sequential Hypothesis Testing
by Betting
ABSTRACT: Developing algorithms to differentiate between machine-generated texts and
human-written texts has garnered substantial attention in recent years.
Existing methods in this direction typically concern an offline sett... | no_new_dataset | 0.940626 |
2410.23780 | Maixuan Xue | Xinyuan Chang, Maixuan Xue, Xinran Liu, Zheng Pan, Xing Wei | Driving by the Rules: A Benchmark for Integrating Traffic Sign
Regulations into Vectorized HD Map | 26 pages, 16 figures. Accepted as a Highlight at CVPR 2025. Project
page: https://miv-xjtu.github.io/MapDR/ | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Ensuring adherence to traffic sign regulations is essential for both human
and autonomous vehicle navigation. While current online mapping solutions often
prioritize the construction of the geometric and connectivity layers of HD
maps, overlooking the construction of the traffic regulation layer within HD
maps. Addre... | [
{
"version": "v1",
"created": "Thu, 31 Oct 2024 09:53:21 GMT"
},
{
"version": "v2",
"created": "Mon, 6 Jan 2025 12:07:55 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 11:13:00 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Chang",
"Xinyuan",
""
],
[
"Xue",
"Maixuan",
""
],
[
"Liu",
"Xinran",
""
],
[
"Pan",
"Zheng",
""
],
[
"Wei",
"Xing",
""
]
] | TITLE: Driving by the Rules: A Benchmark for Integrating Traffic Sign
Regulations into Vectorized HD Map
ABSTRACT: Ensuring adherence to traffic sign regulations is essential for both human
and autonomous vehicle navigation. While current online mapping solutions often
prioritize the construction of the geometric... | new_dataset | 0.958731 |
2411.08033 | Yushi Lan | Yushi Lan, Shangchen Zhou, Zhaoyang Lyu, Fangzhou Hong, Shuai Yang, Bo
Dai, Xingang Pan, Chen Change Loy | GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object
Generation | ICLR 2025 project page: https://nirvanalan.github.io/projects/GA/ | null | null | null | cs.CV cs.AI cs.GR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | While 3D content generation has advanced significantly, existing methods
still face challenges with input formats, latent space design, and output
representations. This paper introduces a novel 3D generation framework that
addresses these challenges, offering scalable, high-quality 3D generation with
an interactive P... | [
{
"version": "v1",
"created": "Tue, 12 Nov 2024 18:59:32 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 12:24:52 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Lan",
"Yushi",
""
],
[
"Zhou",
"Shangchen",
""
],
[
"Lyu",
"Zhaoyang",
""
],
[
"Hong",
"Fangzhou",
""
],
[
"Yang",
"Shuai",
""
],
[
"Dai",
"Bo",
""
],
[
"Pan",
"Xingang",
""
],
[
"Loy",
"Chen C... | TITLE: GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object
Generation
ABSTRACT: While 3D content generation has advanced significantly, existing methods
still face challenges with input formats, latent space design, and output
representations. This paper introduces a novel 3D generation framewor... | no_new_dataset | 0.952662 |
2411.08753 | Sagnik Majumder | Sagnik Majumder, Tushar Nagarajan, Ziad Al-Halah, Reina Pradhan,
Kristen Grauman | Which Viewpoint Shows it Best? Language for Weakly Supervising View
Selection in Multi-view Instructional Videos | Accepted to CVPR 2025 (Highlight) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a multi-view video, which viewpoint is most informative for a human
observer? Existing methods rely on heuristics or expensive "best-view"
supervision to answer this question, limiting their applicability. We propose a
weakly supervised approach that leverages language accompanying an
instructional multi-view v... | [
{
"version": "v1",
"created": "Wed, 13 Nov 2024 16:31:08 GMT"
},
{
"version": "v2",
"created": "Thu, 26 Dec 2024 15:49:20 GMT"
},
{
"version": "v3",
"created": "Fri, 4 Apr 2025 20:45:11 GMT"
},
{
"version": "v4",
"created": "Thu, 10 Apr 2025 02:02:49 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Majumder",
"Sagnik",
""
],
[
"Nagarajan",
"Tushar",
""
],
[
"Al-Halah",
"Ziad",
""
],
[
"Pradhan",
"Reina",
""
],
[
"Grauman",
"Kristen",
""
]
] | TITLE: Which Viewpoint Shows it Best? Language for Weakly Supervising View
Selection in Multi-view Instructional Videos
ABSTRACT: Given a multi-view video, which viewpoint is most informative for a human
observer? Existing methods rely on heuristics or expensive "best-view"
supervision to answer this question, li... | no_new_dataset | 0.951459 |
2411.10739 | Jiangang Chen | Jiangang Chen, Yung-Hong Sun, Kristen Pickett, Barbara King, Yu Hen
Hu, Hongrui Jiang | A Wearable Gait Monitoring System for 17 Gait Parameters Based on
Computer Vision | 13 pages, 14 figures. This paper was submitted for publication to the
IEEE Transactions on Instrumentation and Measurement | null | 10.1109/TIM.2025.3557814 | null | eess.SY cs.CV cs.SY eess.SP | http://creativecommons.org/licenses/by/4.0/ | We developed a shoe-mounted gait monitoring system capable of tracking up to
17 gait parameters, including gait length, step time, stride velocity, and
others. The system employs a stereo camera mounted on one shoe to track a
marker placed on the opposite shoe, enabling the estimation of spatial gait
parameters. Addi... | [
{
"version": "v1",
"created": "Sat, 16 Nov 2024 08:25:22 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Chen",
"Jiangang",
""
],
[
"Sun",
"Yung-Hong",
""
],
[
"Pickett",
"Kristen",
""
],
[
"King",
"Barbara",
""
],
[
"Hu",
"Yu Hen",
""
],
[
"Jiang",
"Hongrui",
""
]
] | TITLE: A Wearable Gait Monitoring System for 17 Gait Parameters Based on
Computer Vision
ABSTRACT: We developed a shoe-mounted gait monitoring system capable of tracking up to
17 gait parameters, including gait length, step time, stride velocity, and
others. The system employs a stereo camera mounted on one shoe ... | no_new_dataset | 0.92421 |
2411.11260 | Cameron Morin | Cameron Morin and Matti Marttinen Larsson | Large corpora and large language models: a replicable method for
automating grammatical annotation | null | Linguistics Vanguard, 1-10 (2025) | 10.1515/lingvan-2024-0228 | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Much linguistic research relies on annotated datasets of features extracted
from text corpora, but the rapid quantitative growth of these corpora has
created practical difficulties for linguists to manually annotate large data
samples. In this paper, we present a replicable, supervised method that
leverages large lan... | [
{
"version": "v1",
"created": "Mon, 18 Nov 2024 03:29:48 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 07:24:50 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Morin",
"Cameron",
""
],
[
"Larsson",
"Matti Marttinen",
""
]
] | TITLE: Large corpora and large language models: a replicable method for
automating grammatical annotation
ABSTRACT: Much linguistic research relies on annotated datasets of features extracted
from text corpora, but the rapid quantitative growth of these corpora has
created practical difficulties for linguists to ... | no_new_dataset | 0.941922 |
2411.15139 | Bencheng Liao | Bencheng Liao, Shaoyu Chen, Haoran Yin, Bo Jiang, Cheng Wang, Sixu
Yan, Xinbang Zhang, Xiangyu Li, Ying Zhang, Qian Zhang, Xinggang Wang | DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous
Driving | Accepted to CVPR 2025 as Highlight. Code & demo & model are available
at https://github.com/hustvl/DiffusionDrive | null | null | null | cs.CV cs.RO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recently, the diffusion model has emerged as a powerful generative technique
for robotic policy learning, capable of modeling multi-mode action
distributions. Leveraging its capability for end-to-end autonomous driving is a
promising direction. However, the numerous denoising steps in the robotic
diffusion policy and... | [
{
"version": "v1",
"created": "Fri, 22 Nov 2024 18:59:47 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Mar 2025 03:02:15 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 08:48:37 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Liao",
"Bencheng",
""
],
[
"Chen",
"Shaoyu",
""
],
[
"Yin",
"Haoran",
""
],
[
"Jiang",
"Bo",
""
],
[
"Wang",
"Cheng",
""
],
[
"Yan",
"Sixu",
""
],
[
"Zhang",
"Xinbang",
""
],
[
"Li",
"Xiangyu",... | TITLE: DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous
Driving
ABSTRACT: Recently, the diffusion model has emerged as a powerful generative technique
for robotic policy learning, capable of modeling multi-mode action
distributions. Leveraging its capability for end-to-end autonomous driving is... | no_new_dataset | 0.945951 |
2411.17060 | Mark Iskarous | Mark M. Iskarous, Zan Chaudhry, Fangjie Li, Samuel Bello, Sriramana
Sankar, Ariel Slepyan, Natasha Chugh, Christopher L. Hunt, Rebecca J. Greene,
Nitish V. Thakor | Invariant neuromorphic representations of tactile stimuli improve
robustness of a real-time texture classification system | 34 pages, 9 figures, 1 table | null | 10.1002/aisy.202401078 | null | cs.RO eess.SP | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Humans have an exquisite sense of touch which robotic and prosthetic systems
aim to recreate. We developed algorithms to create neuron-like (neuromorphic)
spiking representations of texture that are invariant to the scanning speed and
contact force applied in the sensing process. The spiking representations are
based... | [
{
"version": "v1",
"created": "Tue, 26 Nov 2024 02:57:37 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Iskarous",
"Mark M.",
""
],
[
"Chaudhry",
"Zan",
""
],
[
"Li",
"Fangjie",
""
],
[
"Bello",
"Samuel",
""
],
[
"Sankar",
"Sriramana",
""
],
[
"Slepyan",
"Ariel",
""
],
[
"Chugh",
"Natasha",
""
],
[
"... | TITLE: Invariant neuromorphic representations of tactile stimuli improve
robustness of a real-time texture classification system
ABSTRACT: Humans have an exquisite sense of touch which robotic and prosthetic systems
aim to recreate. We developed algorithms to create neuron-like (neuromorphic)
spiking representati... | no_new_dataset | 0.952662 |
2411.19050 | Nicola Fanelli | Nicola Fanelli, Gennaro Vessio, Giovanna Castellano | I Dream My Painting: Connecting MLLMs and Diffusion Models via Prompt
Generation for Text-Guided Multi-Mask Inpainting | Accepted at WACV 2025 | null | 10.1109/WACV61041.2025.00592 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inpainting focuses on filling missing or corrupted regions of an image to
blend seamlessly with its surrounding content and style. While conditional
diffusion models have proven effective for text-guided inpainting, we introduce
the novel task of multi-mask inpainting, where multiple regions are
simultaneously inpain... | [
{
"version": "v1",
"created": "Thu, 28 Nov 2024 10:55:09 GMT"
},
{
"version": "v2",
"created": "Fri, 6 Dec 2024 10:58:53 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Fanelli",
"Nicola",
""
],
[
"Vessio",
"Gennaro",
""
],
[
"Castellano",
"Giovanna",
""
]
] | TITLE: I Dream My Painting: Connecting MLLMs and Diffusion Models via Prompt
Generation for Text-Guided Multi-Mask Inpainting
ABSTRACT: Inpainting focuses on filling missing or corrupted regions of an image to
blend seamlessly with its surrounding content and style. While conditional
diffusion models have proven ... | no_new_dataset | 0.947817 |
2411.19346 | Mohamed Fazli Imam | Mohamed Fazli Imam, Rufael Fedaku Marew, Jameel Hassan, Mustansar
Fiaz, Alham Fikri Aji, Hisham Cholakkal | CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image
Collections | null | null | null | null | cs.CV cs.CL cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In the era of foundation models, CLIP has emerged as a powerful tool for
aligning text & visual modalities into a common embedding space. However, the
alignment objective used to train CLIP often results in subpar visual features
for fine-grained tasks. In contrast, SSL-pretrained models like DINO excel at
extracting... | [
{
"version": "v1",
"created": "Thu, 28 Nov 2024 19:48:54 GMT"
},
{
"version": "v2",
"created": "Fri, 7 Mar 2025 08:08:18 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 11:09:41 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Imam",
"Mohamed Fazli",
""
],
[
"Marew",
"Rufael Fedaku",
""
],
[
"Hassan",
"Jameel",
""
],
[
"Fiaz",
"Mustansar",
""
],
[
"Aji",
"Alham Fikri",
""
],
[
"Cholakkal",
"Hisham",
""
]
] | TITLE: CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image
Collections
ABSTRACT: In the era of foundation models, CLIP has emerged as a powerful tool for
aligning text & visual modalities into a common embedding space. However, the
alignment objective used to train CLIP often results in subpar v... | no_new_dataset | 0.949295 |
2412.01721 | Marc Van Droogenbroeck | Floriane Magera and Thomas Hoyoux and Olivier Barnich and Marc Van
Droogenbroeck | BroadTrack: Broadcast Camera Tracking for Soccer | 12 pages, 4 figures, 3 tables, 60 references | null | 10.1109/wacv61041.2025.00602 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Camera calibration and localization, sometimes simply named camera
calibration, enables many applications in the context of soccer broadcasting,
for instance regarding the interpretation and analysis of the game, or the
insertion of augmented reality graphics for storytelling or refereeing
purposes. To contribute to ... | [
{
"version": "v1",
"created": "Mon, 2 Dec 2024 17:10:52 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Magera",
"Floriane",
""
],
[
"Hoyoux",
"Thomas",
""
],
[
"Barnich",
"Olivier",
""
],
[
"Van Droogenbroeck",
"Marc",
""
]
] | TITLE: BroadTrack: Broadcast Camera Tracking for Soccer
ABSTRACT: Camera calibration and localization, sometimes simply named camera
calibration, enables many applications in the context of soccer broadcasting,
for instance regarding the interpretation and analysis of the game, or the
insertion of augmented reality... | no_new_dataset | 0.930268 |
2412.08864 | Jiankang Wang | Jiankang Wang, Jianjun Xu, Xiaorui Wang, Yuxin Wang, Mengting Xing,
Shancheng Fang, Zhineng Chen, Hongtao Xie, Yongdong Zhang | A Graph-Based Synthetic Data Pipeline for Scaling High-Quality Reasoning
Instructions | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synthesizing high-quality reasoning data for continual training has been
proven to be effective in enhancing the performance of Large Language Models
(LLMs). However, previous synthetic approaches struggle to easily scale up data
and incur high costs in the pursuit of high quality. In this paper, we propose
the Graph... | [
{
"version": "v1",
"created": "Thu, 12 Dec 2024 01:52:25 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 10:47:53 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Wang",
"Jiankang",
""
],
[
"Xu",
"Jianjun",
""
],
[
"Wang",
"Xiaorui",
""
],
[
"Wang",
"Yuxin",
""
],
[
"Xing",
"Mengting",
""
],
[
"Fang",
"Shancheng",
""
],
[
"Chen",
"Zhineng",
""
],
[
"Xie",
... | TITLE: A Graph-Based Synthetic Data Pipeline for Scaling High-Quality Reasoning
Instructions
ABSTRACT: Synthesizing high-quality reasoning data for continual training has been
proven to be effective in enhancing the performance of Large Language Models
(LLMs). However, previous synthetic approaches struggle to ea... | new_dataset | 0.959154 |
2412.08912 | Ali Mollaahmadi Dehaghi | Ali Mollaahmadi Dehaghi, Reza Razavi, Mohammad Moshirpour | Reversing the Damage: A QP-Aware Transformer-Diffusion Approach for 8K
Video Restoration under Codec Compression | 12 pages, 8 figures | null | 10.1109/WACV61041.2025.00130 | null | cs.CV cs.MM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, we introduce DiQP; a novel Transformer-Diffusion model for
restoring 8K video quality degraded by codec compression. To the best of our
knowledge, our model is the first to consider restoring the artifacts
introduced by various codecs (AV1, HEVC) by Denoising Diffusion without
considering additional no... | [
{
"version": "v1",
"created": "Thu, 12 Dec 2024 03:49:22 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Dehaghi",
"Ali Mollaahmadi",
""
],
[
"Razavi",
"Reza",
""
],
[
"Moshirpour",
"Mohammad",
""
]
] | TITLE: Reversing the Damage: A QP-Aware Transformer-Diffusion Approach for 8K
Video Restoration under Codec Compression
ABSTRACT: In this paper, we introduce DiQP; a novel Transformer-Diffusion model for
restoring 8K video quality degraded by codec compression. To the best of our
knowledge, our model is the first... | no_new_dataset | 0.948442 |
2412.14602 | Yuxuan Liang | Yuxuan Liang, Wentao Zhang, Zeang Sheng, Ling Yang, Quanqing Xu,
Jiawei Jiang, Yunhai Tong, Bin Cui | Towards Scalable and Deep Graph Neural Networks via Noise Masking | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, Graph Neural Networks (GNNs) have achieved remarkable
success in many graph mining tasks. However, scaling them to large graphs is
challenging due to the high computational and storage costs of repeated feature
propagation and non-linear transformation during training. One commonly
employed approach ... | [
{
"version": "v1",
"created": "Thu, 19 Dec 2024 07:48:14 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 02:16:19 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Liang",
"Yuxuan",
""
],
[
"Zhang",
"Wentao",
""
],
[
"Sheng",
"Zeang",
""
],
[
"Yang",
"Ling",
""
],
[
"Xu",
"Quanqing",
""
],
[
"Jiang",
"Jiawei",
""
],
[
"Tong",
"Yunhai",
""
],
[
"Cui",
"Bin... | TITLE: Towards Scalable and Deep Graph Neural Networks via Noise Masking
ABSTRACT: In recent years, Graph Neural Networks (GNNs) have achieved remarkable
success in many graph mining tasks. However, scaling them to large graphs is
challenging due to the high computational and storage costs of repeated feature
propa... | no_new_dataset | 0.943764 |
2412.14719 | Kun Li | Kun Li, Dan Guo, Guoliang Chen, Chunxiao Fan, Jingyuan Xu, Zhiliang
Wu, Hehe Fan, Meng Wang | Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition | Fix typos; Accepted by AAAI 2025 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Micro-Action Recognition (MAR) has gained increasing attention due to its
crucial role as a form of non-verbal communication in social interactions, with
promising potential for applications in human communication and emotion
analysis. However, current approaches often overlook the inherent ambiguity in
micro-actions... | [
{
"version": "v1",
"created": "Thu, 19 Dec 2024 10:41:24 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 05:13:15 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Li",
"Kun",
""
],
[
"Guo",
"Dan",
""
],
[
"Chen",
"Guoliang",
""
],
[
"Fan",
"Chunxiao",
""
],
[
"Xu",
"Jingyuan",
""
],
[
"Wu",
"Zhiliang",
""
],
[
"Fan",
"Hehe",
""
],
[
"Wang",
"Meng",
"... | TITLE: Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition
ABSTRACT: Micro-Action Recognition (MAR) has gained increasing attention due to its
crucial role as a form of non-verbal communication in social interactions, with
promising potential for applications in human communication and emotion
a... | no_new_dataset | 0.948728 |
2412.17534 | Ege Yi\u{g}it \c{C}elik | Ege Yi\u{g}it \c{C}elik and Selma Tekir | CiteBART: Learning to Generate Citations for Local Citation
Recommendation | 17 pages, 2 figures, 10 tables | null | null | null | cs.IR cs.AI cs.CL | http://creativecommons.org/licenses/by/4.0/ | Local citation recommendation (LCR) suggests a set of papers for a citation
placeholder within a given context. The task has evolved as generative
approaches have become more promising than the traditional pre-fetch and
re-rank-based state-of-the-art approaches. This paper introduces
citation-specific pre-training wi... | [
{
"version": "v1",
"created": "Mon, 23 Dec 2024 12:58:30 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 20:23:16 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Çelik",
"Ege Yiğit",
""
],
[
"Tekir",
"Selma",
""
]
] | TITLE: CiteBART: Learning to Generate Citations for Local Citation
Recommendation
ABSTRACT: Local citation recommendation (LCR) suggests a set of papers for a citation
placeholder within a given context. The task has evolved as generative
approaches have become more promising than the traditional pre-fetch and
re... | no_new_dataset | 0.953622 |
2412.20439 | Wangyu Wu | Wangyu Wu, Xianglin Qiu, Siqi Song, Zhenhong Chen, Xiaowei Huang, Fei
Ma, Jimin Xiao | Image Augmentation Agent for Weakly Supervised Semantic Segmentation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Weakly-supervised semantic segmentation (WSSS) has achieved remarkable
progress using only image-level labels. However, most existing WSSS methods
focus on designing new network structures and loss functions to generate more
accurate dense labels, overlooking the limitations imposed by fixed datasets,
which can const... | [
{
"version": "v1",
"created": "Sun, 29 Dec 2024 11:32:55 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 08:36:11 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Wu",
"Wangyu",
""
],
[
"Qiu",
"Xianglin",
""
],
[
"Song",
"Siqi",
""
],
[
"Chen",
"Zhenhong",
""
],
[
"Huang",
"Xiaowei",
""
],
[
"Ma",
"Fei",
""
],
[
"Xiao",
"Jimin",
""
]
] | TITLE: Image Augmentation Agent for Weakly Supervised Semantic Segmentation
ABSTRACT: Weakly-supervised semantic segmentation (WSSS) has achieved remarkable
progress using only image-level labels. However, most existing WSSS methods
focus on designing new network structures and loss functions to generate more
accur... | no_new_dataset | 0.94868 |
2412.21037 | Soujanya Poria | Chia-Yu Hung, Navonil Majumder, Zhifeng Kong, Ambuj Mehrish, Amir Ali
Bagherzadeh, Chuan Li, Rafael Valle, Bryan Catanzaro, Soujanya Poria | TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow
Matching and Clap-Ranked Preference Optimization | https://tangoflux.github.io/ | null | null | null | cs.SD cs.AI cs.CL eess.AS | http://creativecommons.org/licenses/by-sa/4.0/ | We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model
with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio
in just 3.7 seconds on a single A40 GPU. A key challenge in aligning TTA models
lies in the difficulty of creating preference pairs, as TTA lacks structured
mechanism... | [
{
"version": "v1",
"created": "Mon, 30 Dec 2024 16:02:44 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 05:01:32 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Hung",
"Chia-Yu",
""
],
[
"Majumder",
"Navonil",
""
],
[
"Kong",
"Zhifeng",
""
],
[
"Mehrish",
"Ambuj",
""
],
[
"Bagherzadeh",
"Amir Ali",
""
],
[
"Li",
"Chuan",
""
],
[
"Valle",
"Rafael",
""
],
[
... | TITLE: TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow
Matching and Clap-Ranked Preference Optimization
ABSTRACT: We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model
with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio
in just 3.7 seconds on a s... | no_new_dataset | 0.943815 |
2501.05449 | MD Arafat Alam Khandaker Arafat | Md. Arafat Alam Khandaker, Ziyan Shirin Raha, Shifat Islam, Tashreef
Muhammad | Explainable AI-Enhanced Deep Learning for Pumpkin Leaf Disease
Detection: A Comparative Analysis of CNN Architectures | Accepted in 2024 27th International Conference on Computer and
Information Technology (ICCIT) | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Pumpkin leaf diseases are significant threats to agricultural productivity,
requiring a timely and precise diagnosis for effective management. Traditional
identification methods are laborious and susceptible to human error,
emphasizing the necessity for automated solutions. This study employs on the
"Pumpkin Leaf Dis... | [
{
"version": "v1",
"created": "Thu, 9 Jan 2025 18:59:35 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 17:35:24 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Khandaker",
"Md. Arafat Alam",
""
],
[
"Raha",
"Ziyan Shirin",
""
],
[
"Islam",
"Shifat",
""
],
[
"Muhammad",
"Tashreef",
""
]
] | TITLE: Explainable AI-Enhanced Deep Learning for Pumpkin Leaf Disease
Detection: A Comparative Analysis of CNN Architectures
ABSTRACT: Pumpkin leaf diseases are significant threats to agricultural productivity,
requiring a timely and precise diagnosis for effective management. Traditional
identification methods a... | no_new_dataset | 0.897471 |
2501.06465 | Ye Chen | Ye Chen, Dongdong Huang, Haoyun Xu, Cong Fu, Lin Sheng, Qingli Zhou,
Yuqiang Shen, Kai Wang | MedCT: A Clinical Terminology Graph for Generative AI Applications in
Healthcare | Accepted into ICCS 2025 and published in Springer's LNCS Series | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | We introduce the world's first clinical terminology for the Chinese
healthcare community, namely MedCT, accompanied by a clinical foundation model
MedBERT and an entity linking model MedLink. The MedCT system enables
standardized and programmable representation of Chinese clinical data,
successively stimulating the d... | [
{
"version": "v1",
"created": "Sat, 11 Jan 2025 07:35:51 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Jan 2025 01:56:11 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 07:29:10 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Chen",
"Ye",
""
],
[
"Huang",
"Dongdong",
""
],
[
"Xu",
"Haoyun",
""
],
[
"Fu",
"Cong",
""
],
[
"Sheng",
"Lin",
""
],
[
"Zhou",
"Qingli",
""
],
[
"Shen",
"Yuqiang",
""
],
[
"Wang",
"Kai",
"... | TITLE: MedCT: A Clinical Terminology Graph for Generative AI Applications in
Healthcare
ABSTRACT: We introduce the world's first clinical terminology for the Chinese
healthcare community, namely MedCT, accompanied by a clinical foundation model
MedBERT and an entity linking model MedLink. The MedCT system enables... | no_new_dataset | 0.947478 |
2501.07824 | Joonho Ko | Joonho Ko, Jinheon Baek, Sung Ju Hwang | Real-time Verification and Refinement of Language Model Text Generation | null | null | null | null | cs.CL cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models (LLMs) have shown remarkable performance across a wide
range of natural language tasks. However, a critical challenge remains in that
they sometimes generate factually incorrect answers. To address this, while
many previous work has focused on identifying errors in their generation and
further r... | [
{
"version": "v1",
"created": "Tue, 14 Jan 2025 03:59:48 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Feb 2025 13:26:52 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 06:39:35 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Ko",
"Joonho",
""
],
[
"Baek",
"Jinheon",
""
],
[
"Hwang",
"Sung Ju",
""
]
] | TITLE: Real-time Verification and Refinement of Language Model Text Generation
ABSTRACT: Large language models (LLMs) have shown remarkable performance across a wide
range of natural language tasks. However, a critical challenge remains in that
they sometimes generate factually incorrect answers. To address this, w... | no_new_dataset | 0.948537 |
2501.14174 | Junyeob Baek | Junyeob Baek, Yi-Fu Wu, Gautam Singh, Sungjin Ahn | Dreamweaver: Learning Compositional World Models from Pixels | null | null | null | null | cs.CV cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Humans have an innate ability to decompose their perceptions of the world
into objects and their attributes, such as colors, shapes, and movement
patterns. This cognitive process enables us to imagine novel futures by
recombining familiar concepts. However, replicating this ability in artificial
intelligence systems ... | [
{
"version": "v1",
"created": "Fri, 24 Jan 2025 01:50:19 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Feb 2025 08:16:03 GMT"
},
{
"version": "v3",
"created": "Thu, 27 Feb 2025 16:09:15 GMT"
},
{
"version": "v4",
"created": "Fri, 28 Feb 2025 08:12:21 GMT"
},
{
"v... | 2025-04-11T00:00:00 | [
[
"Baek",
"Junyeob",
""
],
[
"Wu",
"Yi-Fu",
""
],
[
"Singh",
"Gautam",
""
],
[
"Ahn",
"Sungjin",
""
]
] | TITLE: Dreamweaver: Learning Compositional World Models from Pixels
ABSTRACT: Humans have an innate ability to decompose their perceptions of the world
into objects and their attributes, such as colors, shapes, and movement
patterns. This cognitive process enables us to imagine novel futures by
recombining familiar... | no_new_dataset | 0.942665 |
2501.15293 | Sajjad Saleem | Rubab Hafeez, Sadia Waheed, Syeda Aleena Naqvi, Fahad Maqbool, Amna
Sarwar, Sajjad Saleem, Muhammad Imran Sharif, Kamran Siddique, Zahid Akhtar | Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive
Survey of Classification, Segmentation, and Feature Extraction Methods | 22 pages | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Alzheimers disease is a deadly neurological condition, impairing important
memory and brain functions. Alzheimers disease promotes brain shrinkage,
ultimately leading to dementia. Dementia diagnosis typically takes 2.8 to 4.4
years after the first clinical indication. Advancements in computing and
information technol... | [
{
"version": "v1",
"created": "Sat, 25 Jan 2025 18:00:17 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Jan 2025 10:30:35 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 22:39:50 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Hafeez",
"Rubab",
""
],
[
"Waheed",
"Sadia",
""
],
[
"Naqvi",
"Syeda Aleena",
""
],
[
"Maqbool",
"Fahad",
""
],
[
"Sarwar",
"Amna",
""
],
[
"Saleem",
"Sajjad",
""
],
[
"Sharif",
"Muhammad Imran",
""
],
... | TITLE: Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive
Survey of Classification, Segmentation, and Feature Extraction Methods
ABSTRACT: Alzheimers disease is a deadly neurological condition, impairing important
memory and brain functions. Alzheimers disease promotes brain shrinkage,
ultima... | no_new_dataset | 0.940517 |
2502.04790 | Yuting Zeng | Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, Xitai Jin, Chen
Tianying Tiana, Jing Li, Xiaohua Xu | S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate
Efficiency | Accepted to NAACL 2025 Main | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large language models (LLMs) have demonstrated remarkable capabilities across
various natural language processing (NLP) scenarios, but they still face
challenges when handling complex arithmetic and logical reasoning tasks. While
Chain-Of-Thought (CoT) reasoning, self-consistency (SC) and self-correction
strategies h... | [
{
"version": "v1",
"created": "Fri, 7 Feb 2025 09:49:56 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 02:29:35 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Zeng",
"Yuting",
""
],
[
"Huang",
"Weizhe",
""
],
[
"Jiang",
"Lei",
""
],
[
"Liu",
"Tongxuan",
""
],
[
"Jin",
"Xitai",
""
],
[
"Tiana",
"Chen Tianying",
""
],
[
"Li",
"Jing",
""
],
[
"Xu",
"Xia... | TITLE: S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate
Efficiency
ABSTRACT: Large language models (LLMs) have demonstrated remarkable capabilities across
various natural language processing (NLP) scenarios, but they still face
challenges when handling complex arithmetic and logical reasoning t... | no_new_dataset | 0.948965 |
2502.05780 | Danny Wang | Danny Wang, Ruihong Qiu, Guangdong Bai, Zi Huang | GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial
Latent Generation | ICLR25 | null | null | null | cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Despite graph neural networks' (GNNs) great success in modelling
graph-structured data, out-of-distribution (OOD) test instances still pose a
great challenge for current GNNs. One of the most effective techniques to
detect OOD nodes is to expose the detector model with an additional OOD
node-set, yet the extra OOD in... | [
{
"version": "v1",
"created": "Sun, 9 Feb 2025 05:19:53 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 11:41:23 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Wang",
"Danny",
""
],
[
"Qiu",
"Ruihong",
""
],
[
"Bai",
"Guangdong",
""
],
[
"Huang",
"Zi",
""
]
] | TITLE: GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial
Latent Generation
ABSTRACT: Despite graph neural networks' (GNNs) great success in modelling
graph-structured data, out-of-distribution (OOD) test instances still pose a
great challenge for current GNNs. One of the most effective techniques... | no_new_dataset | 0.949949 |
2502.06193 | Ruiqi Wang | Ruiqi Wang, Jiyu Guo, Cuiyun Gao, Guodong Fan, Chun Yong Chong, Xin
Xia | Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge
in Software Engineering | Accepted by ISSTA 2025:
https://conf.researchr.org/details/issta-2025/issta-2025-papers/85/Can-LLMs-replace-Human-Evaluators-An-Empirical-Study-of-LLM-as-a-Judge-in-Software-E | null | 10.1145/3728963 | null | cs.SE cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recently, large language models (LLMs) have been deployed to tackle various
software engineering (SE) tasks like code generation, significantly advancing
the automation of SE tasks. However, assessing the quality of these
LLM-generated code and text remains challenging. The commonly used Pass@k
metric necessitates ex... | [
{
"version": "v1",
"created": "Mon, 10 Feb 2025 06:49:29 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 07:33:55 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Wang",
"Ruiqi",
""
],
[
"Guo",
"Jiyu",
""
],
[
"Gao",
"Cuiyun",
""
],
[
"Fan",
"Guodong",
""
],
[
"Chong",
"Chun Yong",
""
],
[
"Xia",
"Xin",
""
]
] | TITLE: Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge
in Software Engineering
ABSTRACT: Recently, large language models (LLMs) have been deployed to tackle various
software engineering (SE) tasks like code generation, significantly advancing
the automation of SE tasks. However, assessing ... | no_new_dataset | 0.941815 |
2502.07532 | Erik Larsson | Erik Larsson, Joel Oskarsson, Tomas Landelius, Fredrik Lindsten | Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with
Diffusion | Accepted, camera ready version | null | null | null | cs.LG physics.ao-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning methods have been shown to be effective for weather
forecasting, based on the speed and accuracy compared to traditional numerical
models. While early efforts primarily concentrated on deterministic
predictions, the field has increasingly shifted toward probabilistic
forecasting to better capture the... | [
{
"version": "v1",
"created": "Tue, 11 Feb 2025 13:15:16 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Feb 2025 13:55:08 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 12:10:33 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Larsson",
"Erik",
""
],
[
"Oskarsson",
"Joel",
""
],
[
"Landelius",
"Tomas",
""
],
[
"Lindsten",
"Fredrik",
""
]
] | TITLE: Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with
Diffusion
ABSTRACT: Machine learning methods have been shown to be effective for weather
forecasting, based on the speed and accuracy compared to traditional numerical
models. While early efforts primarily concentrated on deterministic
pred... | no_new_dataset | 0.948585 |
2502.09535 | Carlton Shepherd | Carlton Shepherd and Elliot Hurley | Entropy Collapse in Mobile Sensors: The Hidden Risks of Sensor-Based
Security | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | Mobile sensor data has been proposed for security-critical applications such
as device pairing, proximity detection, and continuous authentication. However,
the foundational premise that these signals provide sufficient entropy remains
under-explored. In this work, we systematically analyse the entropy of mobile
sens... | [
{
"version": "v1",
"created": "Thu, 13 Feb 2025 17:50:58 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Feb 2025 10:43:47 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Feb 2025 22:41:20 GMT"
},
{
"version": "v4",
"created": "Tue, 25 Mar 2025 11:42:52 GMT"
},
{
"v... | 2025-04-11T00:00:00 | [
[
"Shepherd",
"Carlton",
""
],
[
"Hurley",
"Elliot",
""
]
] | TITLE: Entropy Collapse in Mobile Sensors: The Hidden Risks of Sensor-Based
Security
ABSTRACT: Mobile sensor data has been proposed for security-critical applications such
as device pairing, proximity detection, and continuous authentication. However,
the foundational premise that these signals provide sufficient... | no_new_dataset | 0.947721 |
2502.10122 | Saul Jos\'e Rodrigues Dos Santos | Saul Santos and Ant\'onio Farinhas and Daniel C. McNamee and Andr\'e
F.T. Martins | Modern Hopfield Networks with Continuous-Time Memories | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent research has established a connection between modern Hopfield networks
(HNs) and transformer attention heads, with guarantees of exponential storage
capacity. However, these models still face challenges scaling storage
efficiently. Inspired by psychological theories of continuous neural resource
allocation in ... | [
{
"version": "v1",
"created": "Fri, 14 Feb 2025 12:41:05 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Mar 2025 10:06:12 GMT"
},
{
"version": "v3",
"created": "Mon, 24 Mar 2025 17:57:09 GMT"
},
{
"version": "v4",
"created": "Thu, 10 Apr 2025 14:32:13 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Santos",
"Saul",
""
],
[
"Farinhas",
"António",
""
],
[
"McNamee",
"Daniel C.",
""
],
[
"Martins",
"André F. T.",
""
]
] | TITLE: Modern Hopfield Networks with Continuous-Time Memories
ABSTRACT: Recent research has established a connection between modern Hopfield networks
(HNs) and transformer attention heads, with guarantees of exponential storage
capacity. However, these models still face challenges scaling storage
efficiently. Inspi... | no_new_dataset | 0.947478 |
2502.14792 | Henrique Pi\~neiro Monteagudo | Henrique Pi\~neiro Monteagudo, Leonardo Taccari, Aurel Pjetri,
Francesco Sambo, Samuele Salti | RendBEV: Semantic Novel View Synthesis for Self-Supervised Bird's Eye
View Segmentation | Accepted at WACV 2025 | 2025 IEEE/CVF Winter Conference on Applications of Computer Vision
(WACV), Tucson, AZ, USA, 2025, pp. 535-544 | 10.1109/WACV61041.2025.00062 | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Bird's Eye View (BEV) semantic maps have recently garnered a lot of attention
as a useful representation of the environment to tackle assisted and autonomous
driving tasks. However, most of the existing work focuses on the fully
supervised setting, training networks on large annotated datasets. In this
work, we prese... | [
{
"version": "v1",
"created": "Thu, 20 Feb 2025 18:11:44 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 15:00:28 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Monteagudo",
"Henrique Piñeiro",
""
],
[
"Taccari",
"Leonardo",
""
],
[
"Pjetri",
"Aurel",
""
],
[
"Sambo",
"Francesco",
""
],
[
"Salti",
"Samuele",
""
]
] | TITLE: RendBEV: Semantic Novel View Synthesis for Self-Supervised Bird's Eye
View Segmentation
ABSTRACT: Bird's Eye View (BEV) semantic maps have recently garnered a lot of attention
as a useful representation of the environment to tackle assisted and autonomous
driving tasks. However, most of the existing work f... | no_new_dataset | 0.951323 |
2503.00961 | Md Abrar Jahin | Md Abrar Jahin, Shahriar Soudeep, M. F. Mridha, Raihan Kabir, Md
Rashedul Islam, Yutaka Watanobe | CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for
Network Intrusion Detection | Accepted in 38th International Conference on Industrial, Engineering
& Other Applications of Applied Intelligent Systems (IEA/AIE 2025),
Kitakyushu, Japan, Jul 2025 | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Cybersecurity threats are growing, making network intrusion detection
essential. Traditional machine learning models remain effective in
resource-limited environments due to their efficiency, requiring fewer
parameters and less computational time. However, handling short and highly
imbalanced datasets remains challen... | [
{
"version": "v1",
"created": "Sun, 2 Mar 2025 17:01:00 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 15:11:48 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Jahin",
"Md Abrar",
""
],
[
"Soudeep",
"Shahriar",
""
],
[
"Mridha",
"M. F.",
""
],
[
"Kabir",
"Raihan",
""
],
[
"Islam",
"Md Rashedul",
""
],
[
"Watanobe",
"Yutaka",
""
]
] | TITLE: CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for
Network Intrusion Detection
ABSTRACT: Cybersecurity threats are growing, making network intrusion detection
essential. Traditional machine learning models remain effective in
resource-limited environments due to their efficiency, requ... | no_new_dataset | 0.946843 |
2503.01284 | Md Abrar Jahin | Md Abrar Jahin, Soudeep Shahriar, M. F. Mridha, Md. Jakir Hossen,
Nilanjan Dey | Soybean Disease Detection via Interpretable Hybrid CNN-GNN: Integrating
MobileNetV2 and GraphSAGE with Cross-Modal Attention | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Soybean leaf disease detection is critical for agricultural productivity but
faces challenges due to visually similar symptoms and limited interpretability
in conventional methods. While Convolutional Neural Networks (CNNs) excel in
spatial feature extraction, they often neglect inter-image relational
dependencies, l... | [
{
"version": "v1",
"created": "Mon, 3 Mar 2025 08:12:09 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 15:14:17 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Jahin",
"Md Abrar",
""
],
[
"Shahriar",
"Soudeep",
""
],
[
"Mridha",
"M. F.",
""
],
[
"Hossen",
"Md. Jakir",
""
],
[
"Dey",
"Nilanjan",
""
]
] | TITLE: Soybean Disease Detection via Interpretable Hybrid CNN-GNN: Integrating
MobileNetV2 and GraphSAGE with Cross-Modal Attention
ABSTRACT: Soybean leaf disease detection is critical for agricultural productivity but
faces challenges due to visually similar symptoms and limited interpretability
in conventional ... | no_new_dataset | 0.950686 |
2503.02606 | Adam Hartshorne | Adam Hartshorne, Allen Paul, Tony Shardlow, Neill D.F. Campbell | ARC-Flow : Articulated, Resolution-Agnostic, Correspondence-Free
Matching and Interpolation of 3D Shapes Under Flow Fields | 23 pages, 20 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work presents a unified framework for the unsupervised prediction of
physically plausible interpolations between two 3D articulated shapes and the
automatic estimation of dense correspondence between them. Interpolation is
modelled as a diffeomorphic transformation using a smooth, time-varying flow
field governe... | [
{
"version": "v1",
"created": "Tue, 4 Mar 2025 13:28:05 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 22:08:51 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Hartshorne",
"Adam",
""
],
[
"Paul",
"Allen",
""
],
[
"Shardlow",
"Tony",
""
],
[
"Campbell",
"Neill D. F.",
""
]
] | TITLE: ARC-Flow : Articulated, Resolution-Agnostic, Correspondence-Free
Matching and Interpolation of 3D Shapes Under Flow Fields
ABSTRACT: This work presents a unified framework for the unsupervised prediction of
physically plausible interpolations between two 3D articulated shapes and the
automatic estimation o... | no_new_dataset | 0.945551 |
2503.06965 | Shining Wang | Shining Wang, Yunlong Wang, Ruiqi Wu, Bingliang Jiao, Wenxuan Wang,
Peng Wang | SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person
Re-Identification in Aerial-Ground Networks | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When discussing the Aerial-Ground Person Re-identification (AGPReID) task, we
face the main challenge of the significant appearance variations caused by
different viewpoints, making identity matching difficult. To address this
issue, previous methods attempt to reduce the differences between viewpoints by
critical at... | [
{
"version": "v1",
"created": "Mon, 10 Mar 2025 06:23:35 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 02:26:27 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Wang",
"Shining",
""
],
[
"Wang",
"Yunlong",
""
],
[
"Wu",
"Ruiqi",
""
],
[
"Jiao",
"Bingliang",
""
],
[
"Wang",
"Wenxuan",
""
],
[
"Wang",
"Peng",
""
]
] | TITLE: SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person
Re-Identification in Aerial-Ground Networks
ABSTRACT: When discussing the Aerial-Ground Person Re-identification (AGPReID) task, we
face the main challenge of the significant appearance variations caused by
different viewpoints, making iden... | no_new_dataset | 0.942082 |
2503.08580 | Justus Karlsson | Justus Karlsson, Yonghao Xu, Amanda Berg, Leif Haglund | Comparing Next-Day Wildfire Predictability of MODIS and VIIRS Satellite
Data | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Multiple studies have performed next-day fire prediction using satellite
imagery. Two main satellites are used to detect wildfires: MODIS and VIIRS.
Both satellites provide fire mask products, called MOD14 and VNP14,
respectively. Studies have used one or the other, but there has been no
comparison between them to de... | [
{
"version": "v1",
"created": "Tue, 11 Mar 2025 16:15:54 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 15:03:37 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Karlsson",
"Justus",
""
],
[
"Xu",
"Yonghao",
""
],
[
"Berg",
"Amanda",
""
],
[
"Haglund",
"Leif",
""
]
] | TITLE: Comparing Next-Day Wildfire Predictability of MODIS and VIIRS Satellite
Data
ABSTRACT: Multiple studies have performed next-day fire prediction using satellite
imagery. Two main satellites are used to detect wildfires: MODIS and VIIRS.
Both satellites provide fire mask products, called MOD14 and VNP14,
res... | no_new_dataset | 0.936634 |
2503.10111 | Zecheng Zhao | Zecheng Zhao, Zhi Chen, Zi Huang, Shazia Sadiq, Tong Chen | Continual Text-to-Video Retrieval with Frame Fusion and Task-Aware
Routing | Accepted at SIGIR 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Text-to-Video Retrieval (TVR) aims to retrieve relevant videos based on
textual queries. However, as video content evolves continuously, adapting TVR
systems to new data remains a critical yet under-explored challenge. In this
paper, we introduce the first benchmark for Continual Text-to-Video Retrieval
(CTVR) to add... | [
{
"version": "v1",
"created": "Thu, 13 Mar 2025 07:10:56 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 07:20:25 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Zhao",
"Zecheng",
""
],
[
"Chen",
"Zhi",
""
],
[
"Huang",
"Zi",
""
],
[
"Sadiq",
"Shazia",
""
],
[
"Chen",
"Tong",
""
]
] | TITLE: Continual Text-to-Video Retrieval with Frame Fusion and Task-Aware
Routing
ABSTRACT: Text-to-Video Retrieval (TVR) aims to retrieve relevant videos based on
textual queries. However, as video content evolves continuously, adapting TVR
systems to new data remains a critical yet under-explored challenge. In ... | no_new_dataset | 0.947527 |
2503.13911 | Yacong Li | Ruobing Jiang, Yacong Li, Haobing Liu, Yanwei Yu | Incorporating Attributes and Multi-Scale Structures for Heterogeneous
Graph Contrastive Learning | null | null | null | null | cs.LG cs.SI | http://creativecommons.org/licenses/by/4.0/ | Heterogeneous graphs (HGs) are composed of multiple types of nodes and edges,
making it more effective in capturing the complex relational structures
inherent in the real world. However, in real-world scenarios, labeled data is
often difficult to obtain, which limits the applicability of semi-supervised
approaches. S... | [
{
"version": "v1",
"created": "Tue, 18 Mar 2025 05:15:21 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 09:07:02 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Jiang",
"Ruobing",
""
],
[
"Li",
"Yacong",
""
],
[
"Liu",
"Haobing",
""
],
[
"Yu",
"Yanwei",
""
]
] | TITLE: Incorporating Attributes and Multi-Scale Structures for Heterogeneous
Graph Contrastive Learning
ABSTRACT: Heterogeneous graphs (HGs) are composed of multiple types of nodes and edges,
making it more effective in capturing the complex relational structures
inherent in the real world. However, in real-world... | no_new_dataset | 0.948965 |
2503.16825 | Xiyue Guo | Xiyue Guo, Jiarui Hu, Junjie Hu, Hujun Bao, Guofeng Zhang | SGFormer: Satellite-Ground Fusion for 3D Semantic Scene Completion | Project Page: https://zju3dv.github.io/sgformer/ | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, camera-based solutions have been extensively explored for scene
semantic completion (SSC). Despite their success in visible areas, existing
methods struggle to capture complete scene semantics due to frequent visual
occlusions. To address this limitation, this paper presents the first
satellite-ground coope... | [
{
"version": "v1",
"created": "Fri, 21 Mar 2025 03:37:08 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 08:47:41 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Guo",
"Xiyue",
""
],
[
"Hu",
"Jiarui",
""
],
[
"Hu",
"Junjie",
""
],
[
"Bao",
"Hujun",
""
],
[
"Zhang",
"Guofeng",
""
]
] | TITLE: SGFormer: Satellite-Ground Fusion for 3D Semantic Scene Completion
ABSTRACT: Recently, camera-based solutions have been extensively explored for scene
semantic completion (SSC). Despite their success in visible areas, existing
methods struggle to capture complete scene semantics due to frequent visual
occlus... | no_new_dataset | 0.947381 |
2503.17845 | Matthias Herp | Matthias Herp, Johannes Brachem, Michael Altenbuchinger, Thomas Kneib | Graphical Transformation Models | 36 pages, 10 Figures, presented at the DAGStat 2025 in Berlin | null | null | null | stat.ME cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Graphical Transformation Models (GTMs) are introduced as a novel approach to
effectively model multivariate data with intricate marginals and complex
dependency structures non-parametrically, while maintaining interpretability
through the identification of varying conditional independencies. GTMs extend
multivariate ... | [
{
"version": "v1",
"created": "Sat, 22 Mar 2025 19:41:15 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 12:45:22 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Herp",
"Matthias",
""
],
[
"Brachem",
"Johannes",
""
],
[
"Altenbuchinger",
"Michael",
""
],
[
"Kneib",
"Thomas",
""
]
] | TITLE: Graphical Transformation Models
ABSTRACT: Graphical Transformation Models (GTMs) are introduced as a novel approach to
effectively model multivariate data with intricate marginals and complex
dependency structures non-parametrically, while maintaining interpretability
through the identification of varying co... | no_new_dataset | 0.948251 |
2503.19949 | Valerii Zuev | Valerii A. Zuev, Elena G. Salmagambetova, Stepan N. Djakov, Lev V.
Utkin | Automated Video-EEG Analysis in Epilepsy Studies: Advances and
Challenges | null | null | null | null | eess.IV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Epilepsy is typically diagnosed through electroencephalography (EEG) and
long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordings
is time-consuming, necessitating automated tools for seizure detection. Recent
advancements in machine learning have shown promise in real-time seizure
detection and p... | [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 15:37:02 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Apr 2025 17:13:16 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 08:16:31 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Zuev",
"Valerii A.",
""
],
[
"Salmagambetova",
"Elena G.",
""
],
[
"Djakov",
"Stepan N.",
""
],
[
"Utkin",
"Lev V.",
""
]
] | TITLE: Automated Video-EEG Analysis in Epilepsy Studies: Advances and
Challenges
ABSTRACT: Epilepsy is typically diagnosed through electroencephalography (EEG) and
long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordings
is time-consuming, necessitating automated tools for seizure detection. ... | no_new_dataset | 0.948442 |
2503.24305 | Jakub Adamczyk | Jakub Adamczyk, Jakub Poziemski, Pawel Siedlecki | Evaluating machine learning models for predicting pesticides toxicity to
honey bees | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Small molecules play a critical role in the biomedical, environmental, and
agrochemical domains, each with distinct physicochemical requirements and
success criteria. Although biomedical research benefits from extensive datasets
and established benchmarks, agrochemical data remain scarce, particularly with
respect to... | [
{
"version": "v1",
"created": "Mon, 31 Mar 2025 16:51:12 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Apr 2025 07:57:14 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Apr 2025 09:38:53 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Adamczyk",
"Jakub",
""
],
[
"Poziemski",
"Jakub",
""
],
[
"Siedlecki",
"Pawel",
""
]
] | TITLE: Evaluating machine learning models for predicting pesticides toxicity to
honey bees
ABSTRACT: Small molecules play a critical role in the biomedical, environmental, and
agrochemical domains, each with distinct physicochemical requirements and
success criteria. Although biomedical research benefits from ext... | no_new_dataset | 0.801548 |
2504.01541 | Meng Yuan | Meng Yuan, Yutian Xiao, Wei Chen, Chu Zhao, Deqing Wang, Fuzhen Zhuang | Hyperbolic Diffusion Recommender Model | null | null | null | null | cs.IR cs.AI | http://creativecommons.org/licenses/by/4.0/ | Diffusion models (DMs) have emerged as the new state-of-the-art family of
deep generative models. To gain deeper insights into the limitations of
diffusion models in recommender systems, we investigate the fundamental
structural disparities between images and items. Consequently, items often
exhibit distinct anisotro... | [
{
"version": "v1",
"created": "Wed, 2 Apr 2025 09:27:40 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 08:02:56 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Yuan",
"Meng",
""
],
[
"Xiao",
"Yutian",
""
],
[
"Chen",
"Wei",
""
],
[
"Zhao",
"Chu",
""
],
[
"Wang",
"Deqing",
""
],
[
"Zhuang",
"Fuzhen",
""
]
] | TITLE: Hyperbolic Diffusion Recommender Model
ABSTRACT: Diffusion models (DMs) have emerged as the new state-of-the-art family of
deep generative models. To gain deeper insights into the limitations of
diffusion models in recommender systems, we investigate the fundamental
structural disparities between images and ... | no_new_dataset | 0.949059 |
2504.03135 | Junkai Zhang | Junkai Zhang, Bin Li, Shoujun Zhou, Yue Du | Hierarchical Modeling for Medical Visual Question Answering with
Cross-Attention Fusion | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Medical Visual Question Answering (Med-VQA) answers clinical questions using
medical images, aiding diagnosis. Designing the MedVQA system holds profound
importance in assisting clinical diagnosis and enhancing diagnostic accuracy.
Building upon this foundation, Hierarchical Medical VQA extends Medical VQA by
organiz... | [
{
"version": "v1",
"created": "Fri, 4 Apr 2025 03:03:12 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 11:52:40 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Zhang",
"Junkai",
""
],
[
"Li",
"Bin",
""
],
[
"Zhou",
"Shoujun",
""
],
[
"Du",
"Yue",
""
]
] | TITLE: Hierarchical Modeling for Medical Visual Question Answering with
Cross-Attention Fusion
ABSTRACT: Medical Visual Question Answering (Med-VQA) answers clinical questions using
medical images, aiding diagnosis. Designing the MedVQA system holds profound
importance in assisting clinical diagnosis and enhancin... | no_new_dataset | 0.945751 |
2504.03783 | Haoyuan Li | Haoyuan Li, Mathias Funk, Jindong Wang, Aaqib Saeed | FAST: Federated Active Learning with Foundation Models for
Communication-efficient Sampling and Training | null | null | null | null | cs.LG cs.AI cs.CV cs.DC | http://creativecommons.org/licenses/by/4.0/ | Federated Active Learning (FAL) has emerged as a promising framework to
leverage large quantities of unlabeled data across distributed clients while
preserving data privacy. However, real-world deployments remain limited by high
annotation costs and communication-intensive sampling processes, particularly
in a cross-... | [
{
"version": "v1",
"created": "Thu, 3 Apr 2025 16:12:03 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 14:42:57 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Li",
"Haoyuan",
""
],
[
"Funk",
"Mathias",
""
],
[
"Wang",
"Jindong",
""
],
[
"Saeed",
"Aaqib",
""
]
] | TITLE: FAST: Federated Active Learning with Foundation Models for
Communication-efficient Sampling and Training
ABSTRACT: Federated Active Learning (FAL) has emerged as a promising framework to
leverage large quantities of unlabeled data across distributed clients while
preserving data privacy. However, real-worl... | no_new_dataset | 0.947284 |
2504.03970 | Dahun Kim | Dahun Kim, AJ Piergiovanni, Ganesh Mallya, Anelia Angelova | VideoComp: Advancing Fine-Grained Compositional and Temporal Alignment
in Video-Text Models | CVPR 2025, project page at
https://github.com/google-deepmind/video_comp | null | null | null | cs.CV cs.AI cs.CL cs.IR | http://creativecommons.org/licenses/by/4.0/ | We introduce VideoComp, a benchmark and learning framework for advancing
video-text compositionality understanding, aimed at improving vision-language
models (VLMs) in fine-grained temporal alignment. Unlike existing benchmarks
focused on static image-text compositionality or isolated single-event videos,
our benchma... | [
{
"version": "v1",
"created": "Fri, 4 Apr 2025 22:24:30 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 10:41:20 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Kim",
"Dahun",
""
],
[
"Piergiovanni",
"AJ",
""
],
[
"Mallya",
"Ganesh",
""
],
[
"Angelova",
"Anelia",
""
]
] | TITLE: VideoComp: Advancing Fine-Grained Compositional and Temporal Alignment
in Video-Text Models
ABSTRACT: We introduce VideoComp, a benchmark and learning framework for advancing
video-text compositionality understanding, aimed at improving vision-language
models (VLMs) in fine-grained temporal alignment. Unli... | no_new_dataset | 0.946745 |
2504.04103 | Jiaxun Zhang | Jiaxun Zhang, Yanchen Guan, Chengyue Wang, Haicheng Liao, Guohui
Zhang, Zhenning Li | LATTE: Lightweight Attention-based Traffic Accident Anticipation Engine | Accept by Information Fusion (Elsevier) | null | null | null | cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurately predicting traffic accidents in real-time is a critical challenge
in autonomous driving, particularly in resource-constrained environments.
Existing solutions often suffer from high computational overhead or fail to
adequately address the uncertainty of evolving traffic scenarios. This paper
introduces LAT... | [
{
"version": "v1",
"created": "Sat, 5 Apr 2025 08:26:50 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 12:51:38 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Zhang",
"Jiaxun",
""
],
[
"Guan",
"Yanchen",
""
],
[
"Wang",
"Chengyue",
""
],
[
"Liao",
"Haicheng",
""
],
[
"Zhang",
"Guohui",
""
],
[
"Li",
"Zhenning",
""
]
] | TITLE: LATTE: Lightweight Attention-based Traffic Accident Anticipation Engine
ABSTRACT: Accurately predicting traffic accidents in real-time is a critical challenge
in autonomous driving, particularly in resource-constrained environments.
Existing solutions often suffer from high computational overhead or fail to
... | no_new_dataset | 0.94887 |
2504.04753 | Jiacheng Wei | Cheng Chen, Jiacheng Wei, Tianrun Chen, Chi Zhang, Xiaofeng Yang,
Shangzhan Zhang, Bingchen Yang, Chuan-Sheng Foo, Guosheng Lin, Qixing Huang,
Fayao Liu | CADCrafter: Generating Computer-Aided Design Models from Unconstrained
Images | Accepted to CVPR2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Creating CAD digital twins from the physical world is crucial for
manufacturing, design, and simulation. However, current methods typically rely
on costly 3D scanning with labor-intensive post-processing. To provide a
user-friendly design process, we explore the problem of reverse engineering
from unconstrained real-... | [
{
"version": "v1",
"created": "Mon, 7 Apr 2025 06:01:35 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 14:54:12 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Chen",
"Cheng",
""
],
[
"Wei",
"Jiacheng",
""
],
[
"Chen",
"Tianrun",
""
],
[
"Zhang",
"Chi",
""
],
[
"Yang",
"Xiaofeng",
""
],
[
"Zhang",
"Shangzhan",
""
],
[
"Yang",
"Bingchen",
""
],
[
"Foo",
... | TITLE: CADCrafter: Generating Computer-Aided Design Models from Unconstrained
Images
ABSTRACT: Creating CAD digital twins from the physical world is crucial for
manufacturing, design, and simulation. However, current methods typically rely
on costly 3D scanning with labor-intensive post-processing. To provide a
u... | new_dataset | 0.968321 |
2504.04834 | Pengju Sun | Pengju Sun, Banglei Guan, Zhenbao Yu, Yang Shang, Qifeng Yu, Daniel
Barath | Learning Affine Correspondences by Integrating Geometric Constraints | Accepted by IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), 2025 | null | null | null | cs.CV | http://creativecommons.org/publicdomain/zero/1.0/ | Affine correspondences have received significant attention due to their
benefits in tasks like image matching and pose estimation. Existing methods for
extracting affine correspondences still have many limitations in terms of
performance; thus, exploring a new paradigm is crucial. In this paper, we
present a new pipe... | [
{
"version": "v1",
"created": "Mon, 7 Apr 2025 08:44:50 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 13:40:31 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Sun",
"Pengju",
""
],
[
"Guan",
"Banglei",
""
],
[
"Yu",
"Zhenbao",
""
],
[
"Shang",
"Yang",
""
],
[
"Yu",
"Qifeng",
""
],
[
"Barath",
"Daniel",
""
]
] | TITLE: Learning Affine Correspondences by Integrating Geometric Constraints
ABSTRACT: Affine correspondences have received significant attention due to their
benefits in tasks like image matching and pose estimation. Existing methods for
extracting affine correspondences still have many limitations in terms of
perf... | no_new_dataset | 0.949716 |
2504.05730 | Teng Shi | Teng Shi, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Yang Song and
Enyun Yu | Unified Generative Search and Recommendation | null | null | null | null | cs.IR | http://creativecommons.org/licenses/by/4.0/ | Modern commercial platforms typically offer both search and recommendation
functionalities to serve diverse user needs, making joint modeling of these
tasks an appealing direction. While prior work has shown that integrating
search and recommendation can be mutually beneficial, it also reveals a
performance trade-off... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 07:03:08 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 06:34:28 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Shi",
"Teng",
""
],
[
"Xu",
"Jun",
""
],
[
"Zhang",
"Xiao",
""
],
[
"Zang",
"Xiaoxue",
""
],
[
"Zheng",
"Kai",
""
],
[
"Song",
"Yang",
""
],
[
"Yu",
"Enyun",
""
]
] | TITLE: Unified Generative Search and Recommendation
ABSTRACT: Modern commercial platforms typically offer both search and recommendation
functionalities to serve diverse user needs, making joint modeling of these
tasks an appealing direction. While prior work has shown that integrating
search and recommendation can... | no_new_dataset | 0.940517 |
2504.06265 | Bojana Rankovi\'c | Bojana Rankovi\'c, Philippe Schwaller | GOLLuM: Gaussian Process Optimized LLMs -- Reframing LLM Finetuning
through Bayesian Optimization | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) can encode complex relationships in their latent
spaces, yet harnessing them for optimization under uncertainty remains
challenging. We address this gap with a novel architecture that reframes LLM
finetuning as Gaussian process (GP) marginal likelihood optimization via deep
kernel methods... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 17:59:57 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 23:45:44 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Ranković",
"Bojana",
""
],
[
"Schwaller",
"Philippe",
""
]
] | TITLE: GOLLuM: Gaussian Process Optimized LLMs -- Reframing LLM Finetuning
through Bayesian Optimization
ABSTRACT: Large Language Models (LLMs) can encode complex relationships in their latent
spaces, yet harnessing them for optimization under uncertainty remains
challenging. We address this gap with a novel arch... | no_new_dataset | 0.947914 |
2504.06293 | Amin Haeri | Amin Haeri, Jonathan Vitrano, Mahdi Ghelichi | Generative AI Enhanced Financial Risk Management Information Retrieval | 10 pages, 3 figures, 2 tables, 1 equation | null | null | null | q-fin.RM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Risk management in finance involves recognizing, evaluating, and addressing
financial risks to maintain stability and ensure regulatory compliance.
Extracting relevant insights from extensive regulatory documents is a complex
challenge requiring advanced retrieval and language models. This paper
introduces RiskData, ... | [
{
"version": "v1",
"created": "Fri, 4 Apr 2025 20:42:38 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 03:08:59 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Haeri",
"Amin",
""
],
[
"Vitrano",
"Jonathan",
""
],
[
"Ghelichi",
"Mahdi",
""
]
] | TITLE: Generative AI Enhanced Financial Risk Management Information Retrieval
ABSTRACT: Risk management in finance involves recognizing, evaluating, and addressing
financial risks to maintain stability and ensure regulatory compliance.
Extracting relevant insights from extensive regulatory documents is a complex
ch... | new_dataset | 0.963231 |
2504.06301 | Mohsen Jenadeleh | Mohsen Jenadeleh, Jon Sneyers, Panqi Jia, Shima Mohammadi, Joao
Ascenso, Dietmar Saupe | Subjective Visual Quality Assessment for High-Fidelity Learning-Based
Image Compression | 7 pages, 5 figures, 3 tables, submitted to QoMEX 2025 | null | null | null | eess.IV cs.CV | http://creativecommons.org/licenses/by-sa/4.0/ | Learning-based image compression methods have recently emerged as promising
alternatives to traditional codecs, offering improved rate-distortion
performance and perceptual quality. JPEG AI represents the latest standardized
framework in this domain, leveraging deep neural networks for high-fidelity
image reconstruct... | [
{
"version": "v1",
"created": "Mon, 7 Apr 2025 15:16:58 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 11:37:08 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Jenadeleh",
"Mohsen",
""
],
[
"Sneyers",
"Jon",
""
],
[
"Jia",
"Panqi",
""
],
[
"Mohammadi",
"Shima",
""
],
[
"Ascenso",
"Joao",
""
],
[
"Saupe",
"Dietmar",
""
]
] | TITLE: Subjective Visual Quality Assessment for High-Fidelity Learning-Based
Image Compression
ABSTRACT: Learning-based image compression methods have recently emerged as promising
alternatives to traditional codecs, offering improved rate-distortion
performance and perceptual quality. JPEG AI represents the late... | new_dataset | 0.963403 |
2504.06643 | Tiange Huang | Tiange Huang and Yongjun Li | AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series
Anomaly Detection | fix img issues | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Unsupervised multivariate time series anomaly detection (UMTSAD) plays a
critical role in various domains, including finance, networks, and sensor
systems. In recent years, due to the outstanding performance of deep learning
in general sequential tasks, many models have been specialized for deep UMTSAD
tasks and have... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:32:59 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 02:37:53 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Huang",
"Tiange",
""
],
[
"Li",
"Yongjun",
""
]
] | TITLE: AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series
Anomaly Detection
ABSTRACT: Unsupervised multivariate time series anomaly detection (UMTSAD) plays a
critical role in various domains, including finance, networks, and sensor
systems. In recent years, due to the outstanding performance of... | no_new_dataset | 0.946695 |
2504.06742 | Alexandra Ertl | Alexandra Ertl, Shuhan Xiao, Stefan Denner, Robin Peretzke, David
Zimmerer, Peter Neher, Fabian Isensee, Klaus Maier-Hein | nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Landmark detection plays a crucial role in medical imaging tasks that rely on
precise spatial localization, including specific applications in diagnosis,
treatment planning, image registration, and surgical navigation. However,
manual annotation is labor-intensive and requires expert knowledge. While deep
learning sh... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 09:53:39 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 07:04:29 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Ertl",
"Alexandra",
""
],
[
"Xiao",
"Shuhan",
""
],
[
"Denner",
"Stefan",
""
],
[
"Peretzke",
"Robin",
""
],
[
"Zimmerer",
"David",
""
],
[
"Neher",
"Peter",
""
],
[
"Isensee",
"Fabian",
""
],
[
"M... | TITLE: nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection
ABSTRACT: Landmark detection plays a crucial role in medical imaging tasks that rely on
precise spatial localization, including specific applications in diagnosis,
treatment planning, image registration, and surgical navigation. However,... | no_new_dataset | 0.947478 |
2504.06752 | Rishubh Parihar | Rishubh Parihar, Vaibhav Agrawal, Sachidanand VS, R. Venkatesh Babu | Compass Control: Multi Object Orientation Control for Text-to-Image
Generation | CVPR 2025 Camera Ready. Project page:
https://rishubhpar.github.io/compasscontrol | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Existing approaches for controlling text-to-image diffusion models, while
powerful, do not allow for explicit 3D object-centric control, such as precise
control of object orientation. In this work, we address the problem of
multi-object orientation control in text-to-image diffusion models. This
enables the generatio... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 10:15:15 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 04:59:11 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Parihar",
"Rishubh",
""
],
[
"Agrawal",
"Vaibhav",
""
],
[
"VS",
"Sachidanand",
""
],
[
"Babu",
"R. Venkatesh",
""
]
] | TITLE: Compass Control: Multi Object Orientation Control for Text-to-Image
Generation
ABSTRACT: Existing approaches for controlling text-to-image diffusion models, while
powerful, do not allow for explicit 3D object-centric control, such as precise
control of object orientation. In this work, we address the probl... | no_new_dataset | 0.822902 |
2504.06801 | Rishubh Parihar | Rishubh Parihar, Srinjay Sarkar, Sarthak Vora, Jogendra Kundu, R.
Venkatesh Babu | MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular
Detection | CVPR 2025 Camera Ready. Project page -
https://rishubhpar.github.io/monoplace3D | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Current monocular 3D detectors are held back by the limited diversity and
scale of real-world datasets. While data augmentation certainly helps, it's
particularly difficult to generate realistic scene-aware augmented data for
outdoor settings. Most current approaches to synthetic data generation focus on
realistic ob... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 11:47:48 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 05:03:02 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Parihar",
"Rishubh",
""
],
[
"Sarkar",
"Srinjay",
""
],
[
"Vora",
"Sarthak",
""
],
[
"Kundu",
"Jogendra",
""
],
[
"Babu",
"R. Venkatesh",
""
]
] | TITLE: MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular
Detection
ABSTRACT: Current monocular 3D detectors are held back by the limited diversity and
scale of real-world datasets. While data augmentation certainly helps, it's
particularly difficult to generate realistic scene-aware augmented data ... | no_new_dataset | 0.94801 |
2504.07083 | Mengchen Zhang | Mengchen Zhang, Tong Wu, Jing Tan, Ziwei Liu, Gordon Wetzstein, Dahua
Lin | GenDoP: Auto-regressive Camera Trajectory Generation as a Director of
Photography | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Camera trajectory design plays a crucial role in video production, serving as
a fundamental tool for conveying directorial intent and enhancing visual
storytelling. In cinematography, Directors of Photography meticulously craft
camera movements to achieve expressive and intentional framing. However,
existing methods ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 17:56:01 GMT"
},
{
"version": "v2",
"created": "Thu, 10 Apr 2025 16:10:15 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Zhang",
"Mengchen",
""
],
[
"Wu",
"Tong",
""
],
[
"Tan",
"Jing",
""
],
[
"Liu",
"Ziwei",
""
],
[
"Wetzstein",
"Gordon",
""
],
[
"Lin",
"Dahua",
""
]
] | TITLE: GenDoP: Auto-regressive Camera Trajectory Generation as a Director of
Photography
ABSTRACT: Camera trajectory design plays a crucial role in video production, serving as
a fundamental tool for conveying directorial intent and enhancing visual
storytelling. In cinematography, Directors of Photography meticu... | new_dataset | 0.958109 |
2504.07100 | Sean O'Brien | Abhay Gupta, Jacob Cheung, Philip Meng, Shayan Sayyed, Austen Liao,
Kevin Zhu, Sean O'Brien | EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large
Language Models | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The diversity of human language, shaped by social, cultural, and regional
influences, presents significant challenges for natural language processing
(NLP) systems. Existing benchmarks often overlook intra-language variations,
leaving speakers of non-standard dialects underserved. To address this gap, we
introduce En... | [
{
"version": "v1",
"created": "Tue, 25 Feb 2025 23:48:33 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Gupta",
"Abhay",
""
],
[
"Cheung",
"Jacob",
""
],
[
"Meng",
"Philip",
""
],
[
"Sayyed",
"Shayan",
""
],
[
"Liao",
"Austen",
""
],
[
"Zhu",
"Kevin",
""
],
[
"O'Brien",
"Sean",
""
]
] | TITLE: EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large
Language Models
ABSTRACT: The diversity of human language, shaped by social, cultural, and regional
influences, presents significant challenges for natural language processing
(NLP) systems. Existing benchmarks often overlook intra-lan... | new_dataset | 0.95803 |
2504.07102 | Chendi Ge | Chendi Ge, Xin Wang, Ziwei Zhang, Yijian Qin, Hong Chen, Haiyang Wu,
Yang Zhang, Yuekui Yang, Wenwu Zhu | Behavior Importance-Aware Graph Neural Architecture Search for
Cross-Domain Recommendation | AAAI 2025 Oral | null | null | null | cs.IR cs.LG | http://creativecommons.org/licenses/by/4.0/ | Cross-domain recommendation (CDR) mitigates data sparsity and cold-start
issues in recommendation systems. While recent CDR approaches using graph
neural networks (GNNs) capture complex user-item interactions, they rely on
manually designed architectures that are often suboptimal and labor-intensive.
Additionally, ex... | [
{
"version": "v1",
"created": "Tue, 11 Mar 2025 04:30:18 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Ge",
"Chendi",
""
],
[
"Wang",
"Xin",
""
],
[
"Zhang",
"Ziwei",
""
],
[
"Qin",
"Yijian",
""
],
[
"Chen",
"Hong",
""
],
[
"Wu",
"Haiyang",
""
],
[
"Zhang",
"Yang",
""
],
[
"Yang",
"Yuekui",
... | TITLE: Behavior Importance-Aware Graph Neural Architecture Search for
Cross-Domain Recommendation
ABSTRACT: Cross-domain recommendation (CDR) mitigates data sparsity and cold-start
issues in recommendation systems. While recent CDR approaches using graph
neural networks (GNNs) capture complex user-item interactio... | no_new_dataset | 0.951006 |
2504.07108 | Roan Schellingerhout | Roan Schellingerhout, Francesco Barile, and Nava Tintarev | OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender
System | 17 pages, 1 figure, 1 table, to be published in the proceedings of
ECIR2025 | null | null | null | cs.IR cs.AI cs.HC | http://creativecommons.org/licenses/by/4.0/ | The use of recommender systems in the recruitment domain has been labeled as
'high-risk' in recent legislation. As a result, strict requirements regarding
explainability and fairness have been put in place to ensure proper treatment
of all involved stakeholders. To allow for stakeholder-specific explainability,
while... | [
{
"version": "v1",
"created": "Mon, 17 Mar 2025 14:12:51 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Schellingerhout",
"Roan",
""
],
[
"Barile",
"Francesco",
""
],
[
"Tintarev",
"Nava",
""
]
] | TITLE: OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender
System
ABSTRACT: The use of recommender systems in the recruitment domain has been labeled as
'high-risk' in recent legislation. As a result, strict requirements regarding
explainability and fairness have been put in place to ensure pro... | no_new_dataset | 0.946051 |
2504.07110 | Kin Sum Liu | Omkar Gurjar, Kin Sum Liu, Praveen Kolli, Utsaw Kumar, Mandar Rahurkar | DashCLIP: Leveraging multimodal models for generating semantic
embeddings for DoorDash | null | null | null | null | cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the success of vision-language models in various generative tasks,
obtaining high-quality semantic representations for products and user intents
is still challenging due to the inability of off-the-shelf models to capture
nuanced relationships between the entities. In this paper, we introduce a joint
training... | [
{
"version": "v1",
"created": "Tue, 18 Mar 2025 20:38:31 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Gurjar",
"Omkar",
""
],
[
"Liu",
"Kin Sum",
""
],
[
"Kolli",
"Praveen",
""
],
[
"Kumar",
"Utsaw",
""
],
[
"Rahurkar",
"Mandar",
""
]
] | TITLE: DashCLIP: Leveraging multimodal models for generating semantic
embeddings for DoorDash
ABSTRACT: Despite the success of vision-language models in various generative tasks,
obtaining high-quality semantic representations for products and user intents
is still challenging due to the inability of off-the-shel... | no_new_dataset | 0.859605 |
2504.07114 | Serina Chang | Serina Chang, Ashton Anderson, Jake M. Hofman | ChatBench: From Static Benchmarks to Human-AI Evaluation | null | null | null | null | cs.CL cs.AI cs.CY cs.HC | http://creativecommons.org/licenses/by/4.0/ | With the rapid adoption of LLM-based chatbots, there is a pressing need to
evaluate what humans and LLMs can achieve together. However, standard
benchmarks, such as MMLU, measure LLM capabilities in isolation (i.e.,
"AI-alone"). Here, we design and conduct a user study to convert MMLU questions
into user-AI conversat... | [
{
"version": "v1",
"created": "Sat, 22 Mar 2025 01:21:40 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Chang",
"Serina",
""
],
[
"Anderson",
"Ashton",
""
],
[
"Hofman",
"Jake M.",
""
]
] | TITLE: ChatBench: From Static Benchmarks to Human-AI Evaluation
ABSTRACT: With the rapid adoption of LLM-based chatbots, there is a pressing need to
evaluate what humans and LLMs can achieve together. However, standard
benchmarks, such as MMLU, measure LLM capabilities in isolation (i.e.,
"AI-alone"). Here, we desi... | new_dataset | 0.956756 |
2504.07115 | Jiali Cheng | Jiali Cheng, Hadi Amiri | EqualizeIR: Mitigating Linguistic Biases in Retrieval Models | NAACL 2025 | NAACL 2025 | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This study finds that existing information retrieval (IR) models show
significant biases based on the linguistic complexity of input queries,
performing well on linguistically simpler (or more complex) queries while
underperforming on linguistically more complex (or simpler) queries. To address
this issue, we propose... | [
{
"version": "v1",
"created": "Sat, 22 Mar 2025 03:24:34 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Cheng",
"Jiali",
""
],
[
"Amiri",
"Hadi",
""
]
] | TITLE: EqualizeIR: Mitigating Linguistic Biases in Retrieval Models
ABSTRACT: This study finds that existing information retrieval (IR) models show
significant biases based on the linguistic complexity of input queries,
performing well on linguistically simpler (or more complex) queries while
underperforming on lin... | no_new_dataset | 0.951323 |
2504.07117 | Nuren Zhaksylyk | Nuren Zhaksylyk, Ibrahim Almakky, Jay Paranjape, S. Swaroop Vedula,
Shameema Sikder, Vishal M. Patel, Mohammad Yaqub | RP-SAM2: Refining Point Prompts for Stable Surgical Instrument
Segmentation | null | null | null | null | q-bio.TO cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Accurate surgical instrument segmentation is essential in cataract surgery
for tasks such as skill assessment and workflow optimization. However, limited
annotated data makes it difficult to develop fully automatic models.
Prompt-based methods like SAM2 offer flexibility yet remain highly sensitive to
the point promp... | [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 18:59:23 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Zhaksylyk",
"Nuren",
""
],
[
"Almakky",
"Ibrahim",
""
],
[
"Paranjape",
"Jay",
""
],
[
"Vedula",
"S. Swaroop",
""
],
[
"Sikder",
"Shameema",
""
],
[
"Patel",
"Vishal M.",
""
],
[
"Yaqub",
"Mohammad",
""
... | TITLE: RP-SAM2: Refining Point Prompts for Stable Surgical Instrument
Segmentation
ABSTRACT: Accurate surgical instrument segmentation is essential in cataract surgery
for tasks such as skill assessment and workflow optimization. However, limited
annotated data makes it difficult to develop fully automatic models... | no_new_dataset | 0.951774 |
2504.07131 | Peng Liu | Peng Liu, Lian Cheng, Benjamin P.Omell, Anthony P.Burgard | Embedding Reliability Verification Constraints into Generation Expansion
Planning | 5 pages,3 figures. IEEE PES general meeting 2025 | null | null | null | cs.AI stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Generation planning approaches face challenges in managing the incompatible
mathematical structures between stochastic production simulations for
reliability assessment and optimization models for generation planning, which
hinders the integration of reliability constraints. This study proposes an
approach to embeddi... | [
{
"version": "v1",
"created": "Sun, 6 Apr 2025 04:58:45 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Liu",
"Peng",
""
],
[
"Cheng",
"Lian",
""
],
[
"Omell",
"Benjamin P.",
""
],
[
"Burgard",
"Anthony P.",
""
]
] | TITLE: Embedding Reliability Verification Constraints into Generation Expansion
Planning
ABSTRACT: Generation planning approaches face challenges in managing the incompatible
mathematical structures between stochastic production simulations for
reliability assessment and optimization models for generation plannin... | new_dataset | 0.961461 |
2504.07132 | Abdulrahman Alhaidari | Abdulrahman Alhaidari, Bhavani Kalal, Balaji Palanisamy, Shamik Sural | SolRPDS: A Dataset for Analyzing Rug Pulls in Solana Decentralized
Finance | Accepted paper to appear in the 15th ACM Conference on Data and
Application Security and Privacy (CODASPY 2025) | null | null | null | cs.CR cs.CE cs.LG | http://creativecommons.org/licenses/by/4.0/ | Rug pulls in Solana have caused significant damage to users interacting with
Decentralized Finance (DeFi). A rug pull occurs when developers exploit users'
trust and drain liquidity from token pools on Decentralized Exchanges (DEXs),
leaving users with worthless tokens. Although rug pulls in Ethereum and Binance
Smar... | [
{
"version": "v1",
"created": "Sun, 6 Apr 2025 11:36:48 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Alhaidari",
"Abdulrahman",
""
],
[
"Kalal",
"Bhavani",
""
],
[
"Palanisamy",
"Balaji",
""
],
[
"Sural",
"Shamik",
""
]
] | TITLE: SolRPDS: A Dataset for Analyzing Rug Pulls in Solana Decentralized
Finance
ABSTRACT: Rug pulls in Solana have caused significant damage to users interacting with
Decentralized Finance (DeFi). A rug pull occurs when developers exploit users'
trust and drain liquidity from token pools on Decentralized Exchan... | new_dataset | 0.966663 |
2504.07135 | Zhang Mingqing | Mingqing Zhang, Qiang Liu, Xiang Tao, Shu Wu, Liang Wang | SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for
Rumor Detection | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the era of rapidly evolving large language models (LLMs), state-of-the-art
rumor detection systems, particularly those based on Message Propagation Trees
(MPTs), which represent a conversation tree with the post as its root and the
replies as its descendants, are facing increasing threats from adversarial
attacks ... | [
{
"version": "v1",
"created": "Mon, 7 Apr 2025 08:20:48 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Zhang",
"Mingqing",
""
],
[
"Liu",
"Qiang",
""
],
[
"Tao",
"Xiang",
""
],
[
"Wu",
"Shu",
""
],
[
"Wang",
"Liang",
""
]
] | TITLE: SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for
Rumor Detection
ABSTRACT: In the era of rapidly evolving large language models (LLMs), state-of-the-art
rumor detection systems, particularly those based on Message Propagation Trees
(MPTs), which represent a conversation tree with the p... | no_new_dataset | 0.948298 |
2504.07137 | Hamed Jelodar | Hamed Jelodar, Samita Bai, Parisa Hamedi, Hesamodin Mohammadian,
Roozbeh Razavi-Far and Ali Ghorbani | Large Language Model (LLM) for Software Security: Code Analysis, Malware
Analysis, Reverse Engineering | null | null | null | null | cs.CR cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) have recently emerged as powerful tools in
cybersecurity, offering advanced capabilities in malware detection, generation,
and real-time monitoring. Numerous studies have explored their application in
cybersecurity, demonstrating their effectiveness in identifying novel malware
variants, ... | [
{
"version": "v1",
"created": "Mon, 7 Apr 2025 22:32:46 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Jelodar",
"Hamed",
""
],
[
"Bai",
"Samita",
""
],
[
"Hamedi",
"Parisa",
""
],
[
"Mohammadian",
"Hesamodin",
""
],
[
"Razavi-Far",
"Roozbeh",
""
],
[
"Ghorbani",
"Ali",
""
]
] | TITLE: Large Language Model (LLM) for Software Security: Code Analysis, Malware
Analysis, Reverse Engineering
ABSTRACT: Large Language Models (LLMs) have recently emerged as powerful tools in
cybersecurity, offering advanced capabilities in malware detection, generation,
and real-time monitoring. Numerous studies... | no_new_dataset | 0.856692 |
2504.07149 | Matthew Nyaaba | Matthew Nyaaba | Glocalizing Generative AI in Education for the Global South: The Design
Case of 21st Century Teacher Educator AI for Ghana | null | null | null | null | cs.CY | http://creativecommons.org/licenses/by/4.0/ | This study presents the design and development of the 21st Century Teacher
Educator for Ghana GPT, a customized Generative AI (GenAI) tool created using
OpenAI's Retrieval-Augmented Generation (RAG) and Interactive Semi-Automated
Prompting Strategy (ISA). Anchored in a Glocalized design approach, this tool
supports p... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 03:28:35 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Nyaaba",
"Matthew",
""
]
] | TITLE: Glocalizing Generative AI in Education for the Global South: The Design
Case of 21st Century Teacher Educator AI for Ghana
ABSTRACT: This study presents the design and development of the 21st Century Teacher
Educator for Ghana GPT, a customized Generative AI (GenAI) tool created using
OpenAI's Retrieval-Au... | no_new_dataset | 0.952442 |
2504.07151 | David Vigouroux | David Vigouroux, Joseba Dalmau, Louis B\'ethune (IRIT, IRIT-ADRIA,
UT3), Victor Boutin | Deep Sturm--Liouville: From Sample-Based to 1D Regularization with
Learnable Orthogonal Basis Functions | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although Artificial Neural Networks (ANNs) have achieved remarkable success
across various tasks, they still suffer from limited generalization. We
hypothesize that this limitation arises from the traditional sample-based
(0--dimensionnal) regularization used in ANNs. To overcome this, we introduce
\textit{Deep Sturm... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:21:13 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Vigouroux",
"David",
"",
"IRIT, IRIT-ADRIA,\n UT3"
],
[
"Dalmau",
"Joseba",
"",
"IRIT, IRIT-ADRIA,\n UT3"
],
[
"Béthune",
"Louis",
"",
"IRIT, IRIT-ADRIA,\n UT3"
],
[
"Boutin",
"Victor",
""
]
] | TITLE: Deep Sturm--Liouville: From Sample-Based to 1D Regularization with
Learnable Orthogonal Basis Functions
ABSTRACT: Although Artificial Neural Networks (ANNs) have achieved remarkable success
across various tasks, they still suffer from limited generalization. We
hypothesize that this limitation arises from ... | no_new_dataset | 0.949576 |
2504.07152 | Matthew Roughan | Matthew Roughan | Evolutionary Generation of Random Surreal Numbers for Benchmarking | To appear in short form in Genetic and Evolutionary Computation
Conference (GECCO '25), 2025 | Genetic and Evolutionary Computation Conference (GECCO '25), July
14--18, 2025, Malaga | 10.1145/3712255.3726584 | null | cs.NE math.CO | http://creativecommons.org/licenses/by/4.0/ | There are many areas of scientific endeavour where large, complex datasets
are needed for benchmarking. Evolutionary computing provides a means towards
creating such sets. As a case study, we consider Conway's Surreal numbers. They
have largely been treated as a theoretical construct, with little effort
towards empir... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:28:42 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Roughan",
"Matthew",
""
]
] | TITLE: Evolutionary Generation of Random Surreal Numbers for Benchmarking
ABSTRACT: There are many areas of scientific endeavour where large, complex datasets
are needed for benchmarking. Evolutionary computing provides a means towards
creating such sets. As a case study, we consider Conway's Surreal numbers. They
... | no_new_dataset | 0.893959 |
2504.07155 | Jonathan Adam Rico | Jonathan Adam Rico, Nagarajan Raghavan, and Senthilnath Jayavelu | Compound Fault Diagnosis for Train Transmission Systems Using Deep
Learning with Fourier-enhanced Representation | Accepted for the 2025 IEEE Conference on Prognostics and Health
Management (ICPHM 2025) | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Fault diagnosis prevents train disruptions by ensuring the stability and
reliability of their transmission systems. Data-driven fault diagnosis models
have several advantages over traditional methods in terms of dealing with
non-linearity, adaptability, scalability, and automation. However, existing
data-driven model... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 09:01:18 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Rico",
"Jonathan Adam",
""
],
[
"Raghavan",
"Nagarajan",
""
],
[
"Jayavelu",
"Senthilnath",
""
]
] | TITLE: Compound Fault Diagnosis for Train Transmission Systems Using Deep
Learning with Fourier-enhanced Representation
ABSTRACT: Fault diagnosis prevents train disruptions by ensuring the stability and
reliability of their transmission systems. Data-driven fault diagnosis models
have several advantages over trad... | no_new_dataset | 0.94887 |
2504.07157 | Xavier Secheresse | Xavier S\'echeresse, Jacques-Yves Guilbert--Ly and Antoine Villedieu
de Torcy | GAAPO: Genetic Algorithmic Applied to Prompt Optimization | 26 pages, 9 figures | null | null | null | cs.NE cs.LG | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) have demonstrated remarkable capabilities across
various tasks, with their performance heavily dependent on the quality of input
prompts \cite{schulhoff2025promptsurvey} \cite{sahoo2025promptengineering}.
While prompt engineering has proven effective, it typically relies on manual
adjustm... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 11:19:42 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Sécheresse",
"Xavier",
""
],
[
"Guilbert--Ly",
"Jacques-Yves",
""
],
[
"de Torcy",
"Antoine Villedieu",
""
]
] | TITLE: GAAPO: Genetic Algorithmic Applied to Prompt Optimization
ABSTRACT: Large Language Models (LLMs) have demonstrated remarkable capabilities across
various tasks, with their performance heavily dependent on the quality of input
prompts \cite{schulhoff2025promptsurvey} \cite{sahoo2025promptengineering}.
While p... | no_new_dataset | 0.944228 |
2504.07165 | Yana Wei | Yana Wei, Liang Zhao, Kangheng Lin, En Yu, Yuang Peng, Runpei Dong,
Jianjian Sun, Haoran Wei, Zheng Ge, Xiangyu Zhang, Vishal M. Patel | Perception in Reflection | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a perception in reflection paradigm designed to transcend the
limitations of current large vision-language models (LVLMs), which are expected
yet often fail to achieve perfect perception initially. Specifically, we
propose Reflective Perception (RePer), a dual-model reflection mechanism that
systematically... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 17:59:02 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Wei",
"Yana",
""
],
[
"Zhao",
"Liang",
""
],
[
"Lin",
"Kangheng",
""
],
[
"Yu",
"En",
""
],
[
"Peng",
"Yuang",
""
],
[
"Dong",
"Runpei",
""
],
[
"Sun",
"Jianjian",
""
],
[
"Wei",
"Haoran",
... | TITLE: Perception in Reflection
ABSTRACT: We present a perception in reflection paradigm designed to transcend the
limitations of current large vision-language models (LVLMs), which are expected
yet often fail to achieve perfect perception initially. Specifically, we
propose Reflective Perception (RePer), a dual-mo... | new_dataset | 0.962143 |
2504.07198 | Ashutosh Chaubey | Ashutosh Chaubey, Xulang Guan, Mohammad Soleymani | Face-LLaVA: Facial Expression and Attribute Understanding through
Instruction Tuning | Project Page: https://face-llava.github.io | null | null | null | cs.CV cs.AI cs.HC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The human face plays a central role in social communication, necessitating
the use of performant computer vision tools for human-centered applications. We
propose Face-LLaVA, a multimodal large language model for face-centered,
in-context learning, including facial expression and attribute recognition.
Additionally, ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 18:26:07 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Chaubey",
"Ashutosh",
""
],
[
"Guan",
"Xulang",
""
],
[
"Soleymani",
"Mohammad",
""
]
] | TITLE: Face-LLaVA: Facial Expression and Attribute Understanding through
Instruction Tuning
ABSTRACT: The human face plays a central role in social communication, necessitating
the use of performant computer vision tools for human-centered applications. We
propose Face-LLaVA, a multimodal large language model for... | new_dataset | 0.959383 |
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