id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.12194 | An End-to-End Approach for Korean Wakeword Systems with Speaker
Authentication | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | Wakeword detection plays a critical role in enabling AI assistants to listen to user voices and interact effectively. However, for languages other than English, there is a significant lack of pre-trained wakeword models. Additionally, systems that merely determine the presence of a wakeword can pose serious privacy con... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 1,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12198 | Opinion dynamics in bounded confidence models with manipulative agents:
Moving the Overton window | [
"cs.SI",
"physics.soc-ph",
"stat.AP"
] | This paper focuses on the opinion dynamics under the influence of manipulative agents. This type of agents is characterized by the fact that their opinions follow a trajectory that does not respond to the dynamics of the model, although it does influence the rest of the normal agents. Simulation has been implemented to... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 1,
"cs.SY": 0
} |
2501.12199 | Experience-replay Innovative Dynamics | [
"cs.LG",
"cs.GT",
"cs.MA"
] | Despite its groundbreaking success, multi-agent reinforcement learning (MARL) still suffers from instability and nonstationarity. Replicator dynamics, the most well-known model from evolutionary game theory (EGT), provide a theoretical framework for the convergence of the trajectories to Nash equilibria and, as a resul... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 1,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12202 | Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D
Assets Generation | [
"cs.CV"
] | We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-scale shape generation model -- Hunyuan3D-DiT, and a large-scale texture synthesis model -- Hunyuan3D-Paint. The shape generative model, built ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12203 | Explainability for Vision Foundation Models: A Survey | [
"cs.CV"
] | As artificial intelligence systems become increasingly integrated into daily life, the field of explainability has gained significant attention. This trend is particularly driven by the complexity of modern AI models and their decision-making processes. The advent of foundation models, characterized by their extensive ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12204 | Score Combining for Contrastive OOD Detection | [
"cs.LG"
] | In out-of-distribution (OOD) detection, one is asked to classify whether a test sample comes from a known inlier distribution or not. We focus on the case where the inlier distribution is defined by a training dataset and there exists no additional knowledge about the novelties that one is likely to encounter. This pro... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12206 | Fixing Imbalanced Attention to Mitigate In-Context Hallucination of
Large Vision-Language Model | [
"cs.CV",
"cs.CL"
] | Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities in understanding and describing visual content, achieving state-of-the-art performance across various vision-language tasks. However, these models frequently exhibit hallucination behavior, where they generate descriptions containing objects... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12208 | Community Discovery Algorithm Based on Spatio-temporal Graph Embedding
in Dynamic Social Networks | [
"cs.SI"
] | Community discovery is one of the key issues in the study of dynamic social networks. Traditional community discovery algorithms only focus on the establishment and disconnection of connections between nodes, failing to capture deeper factors. To address this limitation, in this work, we propose a community discovery a... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 1,
"cs.SY": 0
} |
2501.12212 | Quantitative Error Bounds for Scaling Limits of Stochastic Iterative
Algorithms | [
"stat.ML",
"cs.LG",
"math.PR",
"math.ST",
"stat.TH"
] | Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in large-scale and high-dimensional problems in machine learning, statistics, and engineering. Numerous works have bounded the parameter error ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12214 | Improving robot understanding using conversational AI: demonstration and
feasibility study | [
"cs.RO",
"cs.HC"
] | Explanations constitute an important aspect of successful human robot interactions and can enhance robot understanding. To improve the understanding of the robot, we have developed four levels of explanation (LOE) based on two questions: what needs to be explained, and why the robot has made a particular decision. The ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 1,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12215 | Automatic selection of the best neural architecture for time series
forecasting via multi-objective optimization and Pareto optimality conditions | [
"cs.LG"
] | Time series forecasting plays a pivotal role in a wide range of applications, including weather prediction, healthcare, structural health monitoring, predictive maintenance, energy systems, and financial markets. While models such as LSTM, GRU, Transformers, and State-Space Models (SSMs) have become standard tools in t... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12216 | RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression | [
"cs.LG",
"cs.CV",
"eess.IV"
] | Video encoders optimize compression for human perception by minimizing reconstruction error under bit-rate constraints. In many modern applications such as autonomous driving, an overwhelming majority of videos serve as input for AI systems performing tasks like object recognition or segmentation, rather than being wat... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12217 | Early Detection and Classification of Breast Cancer Using Deep Learning
Techniques | [
"cs.CV",
"cs.LG"
] | Breast cancer is one of the deadliest cancers causing about massive number of patients to die annually all over the world according to the WHO. It is a kind of cancer that develops when the tissues of the breast grow rapidly and unboundly. This fatality rate can be prevented if the cancer is detected before it gets mal... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12218 | Exploring Temporally-Aware Features for Point Tracking | [
"cs.CV"
] | Point tracking in videos is a fundamental task with applications in robotics, video editing, and more. While many vision tasks benefit from pre-trained feature backbones to improve generalizability, point tracking has primarily relied on simpler backbones trained from scratch on synthetic data, which may limit robustne... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12222 | Strong phonon-mediated high temperature superconductivity in
Li$_2$AuH$_6$ under ambient pressure | [
"cond-mat.supr-con",
"cond-mat.mtrl-sci",
"cs.AI",
"physics.comp-ph"
] | We used our developed AI search engine~(InvDesFlow) to perform extensive investigations regarding ambient stable superconducting hydrides. A cubic structure Li$_2$AuH$_6$ with Au-H octahedral motifs is identified to be a candidate. After performing thermodynamical analysis, we provide a feasible route to experimentally... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12224 | TokenVerse: Versatile Multi-concept Personalization in Token Modulation
Space | [
"cs.CV"
] | We present TokenVerse -- a method for multi-concept personalization, leveraging a pre-trained text-to-image diffusion model. Our framework can disentangle complex visual elements and attributes from as little as a single image, while enabling seamless plug-and-play generation of combinations of concepts extracted from ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12226 | CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoning | [
"cs.LG"
] | Large Language Models (LLMs) have recently achieved impressive results in complex reasoning tasks through Chain of Thought (CoT) prompting. However, most existing CoT methods rely on using the same prompts, whether manually designed or automatically generated, to handle the entire dataset. This one-size-fits-all approa... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12227 | Multi-terminal Strong Coordination over Noisy Channels with Encoder
Cooperation | [
"cs.IT",
"math.IT"
] | We investigate the problem of strong coordination over a multiple-access channel (MAC) with cribbing encoders. In this configuration, two encoders observe independent and identically distributed (i.i.d.) samples of a source random variable each and encode the inputs to the MAC. The decoder which observes the output of ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12231 | InsTALL: Context-aware Instructional Task Assistance with Multi-modal
Large Language Models | [
"cs.CV",
"cs.AI",
"cs.CL"
] | The improved competence of generative models can help building multi-modal virtual assistants that leverage modalities beyond language. By observing humans performing multi-step tasks, one can build assistants that have situational awareness of actions and tasks being performed, enabling them to cater assistance based ... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12234 | Multi-Agent Feedback Motion Planning using Probably Approximately
Correct Nonlinear Model Predictive Control | [
"cs.RO"
] | For many tasks, multi-robot teams often provide greater efficiency, robustness, and resiliency. However, multi-robot collaboration in real-world scenarios poses a number of major challenges, especially when dynamic robots must balance competing objectives like formation control and obstacle avoidance in the presence of... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 1,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12235 | DLEN: Dual Branch of Transformer for Low-Light Image Enhancement in Dual
Domains | [
"cs.CV",
"eess.IV"
] | Low-light image enhancement (LLE) aims to improve the visual quality of images captured in poorly lit conditions, which often suffer from low brightness, low contrast, noise, and color distortions. These issues hinder the performance of computer vision tasks such as object detection, facial recognition, and autonomous ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12236 | Fast sparse optimization via adaptive shrinkage | [
"math.OC",
"cs.LG",
"cs.SY",
"eess.SY"
] | The need for fast sparse optimization is emerging, e.g., to deal with large-dimensional data-driven problems and to track time-varying systems. In the framework of linear sparse optimization, the iterative shrinkage-thresholding algorithm is a valuable method to solve Lasso, which is particularly appreciated for its ea... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2501.12239 | Investigating Market Strength Prediction with CNNs on Candlestick Chart
Images | [
"cs.CV"
] | This paper investigates predicting market strength solely from candlestick chart images to assist investment decisions. The core research problem is developing an effective computer vision-based model using raw candlestick visuals without time-series data. We specifically analyze the impact of incorporating candlestick... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12243 | FOCUS: First Order Concentrated Updating Scheme | [
"cs.LG",
"cs.CL",
"math.OC"
] | Large language models (LLMs) demonstrate remarkable performance, and improving their pre-training process appears to be key to enhancing their capabilities further. Based on the documented success of Adam, learning rate decay, and weight decay, we hypothesize that the pre-training loss landscape features a narrowing va... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12244 | Zero-shot Bias Correction: Efficient MR Image Inhomogeneity Reduction
Without Any Data | [
"eess.IV",
"cs.CV"
] | In recent years, deep neural networks for image inhomogeneity reduction have shown promising results. However, current methods with (un)supervised solutions require preparing a training dataset, which is expensive and laborious for data collection. In this work, we demonstrate a novel zero-shot deep neural networks, wh... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12245 | Quality Enhancement of Radiographic X-ray Images by Interpretable
Mapping | [
"eess.IV",
"cs.CV"
] | X-ray imaging is the most widely used medical imaging modality. However, in the common practice, inconsistency in the initial presentation of X-ray images is a common complaint by radiologists. Different patient positions, patient habitus and scanning protocols can lead to differences in image presentations, e.g., diff... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12246 | Video Deblurring by Sharpness Prior Detection and Edge Information | [
"cs.CV"
] | Video deblurring is essential task for autonomous driving, facial recognition, and security surveillance. Traditional methods directly estimate motion blur kernels, often introducing artifacts and leading to poor results. Recent approaches utilize the detection of sharp frames within video sequences to enhance deblurri... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12251 | Solar Panel Selection using Extended WASPAS with Disc Intuitionistic
Fuzzy Choquet Integral Operators: CASPAS Methodology | [
"cs.IT",
"math.IT"
] | Renewable energy is crucial for addressing the growing energy demands of modern society while mitigating the adverse effects of climate change. Unlike fossil fuels, renewable energy sources such as solar, wind, hydro, geothermal, and biomass are abundant, sustainable, and environmentally friendly. This study focuses on... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12254 | Memory Storyboard: Leveraging Temporal Segmentation for Streaming
Self-Supervised Learning from Egocentric Videos | [
"cs.CV",
"cs.LG"
] | Self-supervised learning holds the promise to learn good representations from real-world continuous uncurated data streams. However, most existing works in visual self-supervised learning focus on static images or artificial data streams. Towards exploring a more realistic learning substrate, we investigate streaming s... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12255 | HAC++: Towards 100X Compression of 3D Gaussian Splatting | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis, boasting rapid rendering speed with high fidelity. However, the substantial Gaussians and their associated attributes necessitate effective compression techniques. Nevertheless, the sparse and unorganized nature of the point clo... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12256 | Lie-Bracket Nash Equilibrium Seeking with Bounded Update Rates for
Noncooperative Games | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper proposes a novel approach for local convergence to Nash equilibrium in quadratic noncooperative games based on a distributed Lie-bracket extremum seeking control scheme. This is the first instance of noncooperative games being tackled in a model-free fashion integrated with the extremum seeking method of bou... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2501.12263 | mmCooper: A Multi-agent Multi-stage Communication-efficient and
Collaboration-robust Cooperative Perception Framework | [
"cs.CV"
] | Collaborative perception significantly enhances individual vehicle perception performance through the exchange of sensory information among agents. However, real-world deployment faces challenges due to bandwidth constraints and inevitable calibration errors during information exchange. To address these issues, we prop... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12266 | CBVLM: Training-free Explainable Concept-based Large Vision Language
Models for Medical Image Classification | [
"cs.CV",
"cs.AI",
"cs.CL"
] | The main challenges limiting the adoption of deep learning-based solutions in medical workflows are the availability of annotated data and the lack of interpretability of such systems. Concept Bottleneck Models (CBMs) tackle the latter by constraining the final disease prediction on a set of predefined and human-interp... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12267 | VipDiff: Towards Coherent and Diverse Video Inpainting via Training-free
Denoising Diffusion Models | [
"cs.CV"
] | Recent video inpainting methods have achieved encouraging improvements by leveraging optical flow to guide pixel propagation from reference frames either in the image space or feature space. However, they would produce severe artifacts in the mask center when the masked area is too large and no pixel correspondences ca... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12269 | Benchmarking Image Perturbations for Testing Automated Driving
Assistance Systems | [
"cs.SE",
"cs.CV"
] | Advanced Driver Assistance Systems (ADAS) based on deep neural networks (DNNs) are widely used in autonomous vehicles for critical perception tasks such as object detection, semantic segmentation, and lane recognition. However, these systems are highly sensitive to input variations, such as noise and changes in lightin... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12271 | Faithful Simulation of Distributed Quantum Measurement with Coding for
Computing | [
"cs.IT",
"math.IT"
] | This papers consider a two terminal problem, where Alice and Bob jointly want to perform a measurement on a bipartite quantum system $\rho^{AB}$. Alice can transmit the results of her measurements to Bob on a classical channel, and Alice and Bob have common randomness. The question is what is the minimum amount of comm... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12272 | A Lightweight Approach for User and Keyword Classification in
Controversial Topics | [
"cs.SI"
] | Classifying the stance of individuals on controversial topics and uncovering their concerns is crucial for social scientists and policymakers. Data from Online Social Networks (OSNs), which serve as a proxy to a representative sample of society, offers an opportunity to classify these stances, discover society's concer... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 1,
"cs.SY": 0
} |
2501.12273 | Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and
Refinement | [
"cs.CL",
"cs.AI"
] | The quality of Supervised Fine-Tuning (SFT) data plays a critical role in enhancing the conversational capabilities of Large Language Models (LLMs). However, as LLMs become more advanced, the availability of high-quality human-annotated SFT data has become a significant bottleneck, necessitating a greater reliance on s... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12274 | Making it to First: The Random Access Problem in DNA Storage | [
"cs.IT",
"math.IT"
] | We study the Random Access Problem in DNA storage, which addresses the challenge of retrieving a specific information strand from a DNA-based storage system. Given that $k$ information strands, representing the data, are encoded into $n$ strands using a code. The goal under this paradigm is to identify and analyze code... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12275 | With Great Backbones Comes Great Adversarial Transferability | [
"cs.CV",
"cs.AI",
"cs.CR",
"cs.LG",
"cs.MA"
] | Advances in self-supervised learning (SSL) for machine vision have improved representation robustness and model performance, giving rise to pre-trained backbones like \emph{ResNet} and \emph{ViT} models tuned with SSL methods such as \emph{SimCLR}. Due to the computational and data demands of pre-training, the utilizat... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 1,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12279 | Spatial exponential decay of perturbations in optimal control of general
evolution equations | [
"math.OC",
"cs.SY",
"eess.SY",
"math.AP"
] | We analyze the robustness of optimally controlled evolution equations with respect to spatially localized perturbations. We prove that if the involved operators are domain-uniformly stabilizable and detectable, then these localized perturbations only have a local effect on the optimal solution. We characterize this dom... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2501.12280 | Bounds and Codes for General Phased Burst Errors | [
"cs.IT",
"math.IT"
] | Phased burst errors (PBEs) are bursts of errors occurring at one or more known locations. The correction of PBEs is a classical topic in coding theory, with prominent applications such as the design of array codes for memory systems or distributed storage. We propose a general yet fine-grained approach to this problem,... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12281 | MoGERNN: An Inductive Traffic Predictor for Unobserved Locations in
Dynamic Sensing Networks | [
"cs.LG"
] | Given a partially observed road network, how can we predict the traffic state of unobserved locations? While deep learning approaches show exceptional performance in traffic prediction, most assume sensors at all locations of interest, which is impractical due to financial constraints. Furthermore, these methods typica... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12285 | Implementation of an Asymmetric Adjusted Activation Function for Class
Imbalance Credit Scoring | [
"cs.LG",
"cs.AI",
"q-fin.RM"
] | Credit scoring is a systematic approach to evaluate a borrower's probability of default (PD) on a bank loan. The data associated with such scenarios are characteristically imbalanced, complicating binary classification owing to the often-underestimated cost of misclassification during the classifier's learning process.... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12286 | A Linear Programming Approach to Private Information Retrieval | [
"cs.IT",
"math.IT"
] | This work presents an algorithmic framework that uses linear programming to construct \emph{addition-based Private Information Retrieval (AB-PIR)} schemes, where retrieval is performed by downloading only linear combinations of message symbols with coefficients set to 0 or 1. The AB-PIR schemes generalize several exist... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12288 | Microgrid Operation Control with State-of-Charge- Dependent Storage
Power Constraints | [
"eess.SY",
"cs.SY"
] | The microgrid concept offers high flexibility and resilience due to the possibility of switching between grid-connected and stand-alone operation. This renders microgrids an auspicious solution for rural areas and critical infrastructure. In standalone or islanded mode, the main objective is cost minimization while ens... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2501.12289 | Regressor-Guided Image Editing Regulates Emotional Response to Reduce
Online Engagement | [
"cs.CV",
"cs.AI",
"cs.HC"
] | Emotions are known to mediate the relationship between users' content consumption and their online engagement, with heightened emotional intensity leading to increased engagement. Building on this insight, we propose three regressor-guided image editing approaches aimed at diminishing the emotional impact of images. Th... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 1,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12293 | Improved Decoding of Tanner Codes | [
"cs.IT",
"cs.CC",
"cs.DS",
"math.IT"
] | In this paper, we present improved decoding algorithms for expander-based Tanner codes. We begin by developing a randomized linear-time decoding algorithm that, under the condition that $ \delta d_0 > 2 $, corrects up to $ \alpha n $ errors for a Tanner code $ T(G, C_0) $, where $ G $ is a $ (c, d, \alpha, \delta) $-... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12294 | Wrap-Decoding in Asynchronous Unsourced Multiple Access With and Without
Delay Information | [
"cs.IT",
"math.IT"
] | An asynchronous $\ka$-active-user unsourced multiple access channel (AUMAC) is a key model for uncoordinated massive access in future networks. We focus on a scenario where each transmission is subject to the maximal delay constraint ($\dm$), and the precise delay of each user is unknown at the receiver. The combined e... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12295 | Towards Accurate Unified Anomaly Segmentation | [
"cs.CV"
] | Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for fur... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12296 | RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with
Retrieval-Augmented Learning | [
"cs.CV",
"cs.AI"
] | In the pursuit of robust autonomous driving systems, models trained on real-world datasets often struggle to adapt to new environments, particularly when confronted with corner cases such as extreme weather conditions. Collecting these corner cases in the real world is non-trivial, which necessitates the use of simulat... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12299 | Sublinear Variational Optimization of Gaussian Mixture Models with
Millions to Billions of Parameters | [
"stat.ML",
"cs.CV",
"cs.LG"
] | Gaussian Mixture Models (GMMs) range among the most frequently used machine learning models. However, training large, general GMMs becomes computationally prohibitive for datasets with many data points $N$ of high-dimensionality $D$. For GMMs with arbitrary covariances, we here derive a highly efficient variational app... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12300 | LLM-Assisted Knowledge Graph Completion for Curriculum and Domain
Modelling in Personalized Higher Education Recommendations | [
"cs.HC",
"cs.AI"
] | While learning personalization offers great potential for learners, modern practices in higher education require a deeper consideration of domain models and learning contexts, to develop effective personalization algorithms. This paper introduces an innovative approach to higher education curriculum modelling that util... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 1,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12309 | A Hybrid Supervised and Self-Supervised Graph Neural Network for
Edge-Centric Applications | [
"cs.LG",
"q-bio.MN"
] | This paper presents a novel graph-based deep learning model for tasks involving relations between two nodes (edge-centric tasks), where the focus lies on predicting relationships and interactions between pairs of nodes rather than node properties themselves. This model combines supervised and self-supervised learning, ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12310 | Optimizing Leaky Private Information Retrieval Codes to Achieve
${O}(\log K)$ Leakage Ratio Exponent | [
"cs.IR",
"cs.IT",
"math.IT"
] | We study the problem of leaky private information retrieval (L-PIR), where the amount of privacy leakage is measured by the pure differential privacy parameter, referred to as the leakage ratio exponent. Unlike the previous L-PIR scheme proposed by Samy et al., which only adjusted the probability allocation to the clea... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 1,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12314 | Uncertainty Quantification With Noise Injection in Neural Networks: A
Bayesian Perspective | [
"stat.ML",
"cs.LG"
] | Model uncertainty quantification involves measuring and evaluating the uncertainty linked to a model's predictions, helping assess their reliability and confidence. Noise injection is a technique used to enhance the robustness of neural networks by introducing randomness. In this paper, we establish a connection betwee... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12318 | BlanketGen2-Fit3D: Synthetic Blanket Augmentation Towards Improving
Real-World In-Bed Blanket Occluded Human Pose Estimation | [
"cs.CV"
] | Human Pose Estimation (HPE) from monocular RGB images is crucial for clinical in-bed skeleton-based action recognition, however, it poses unique challenges for HPE models due to the frequent presence of blankets occluding the person, while labeled HPE data in this scenario is scarce. To address this we introduce Blanke... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12319 | Metric for Evaluating Performance of Reference-Free Demorphing Methods | [
"cs.CV"
] | A facial morph is an image created by combining two (or more) face images pertaining to two (or more) distinct identities. Reference-free face demorphing inverts the process and tries to recover the face images constituting a facial morph without using any other information. However, there is no consensus on the evalua... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12322 | A General Achievable Scheme for Linear Computation Broadcast Channel | [
"cs.IT",
"math.IT"
] | This paper presents a new achievable scheme for the Linear Computation Broadcast Channel (LCBC), which is based on a generalized subspace decomposition derived from representable polymatroid space. This decomposition enables the server to serve user demands with an approach of effective multicast and interference elimi... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12323 | Deep Learning Based Segmentation of Blood Vessels from H&E Stained
Oesophageal Adenocarcinoma Whole-Slide Images | [
"eess.IV",
"cs.CV"
] | Blood vessels (BVs) play a critical role in the Tumor Micro-Environment (TME), potentially influencing cancer progression and treatment response. However, manually quantifying BVs in Hematoxylin and Eosin (H&E) stained images is challenging and labor-intensive due to their heterogeneous appearances. We propose a novel ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12326 | UI-TARS: Pioneering Automated GUI Interaction with Native Agents | [
"cs.AI",
"cs.CL",
"cs.CV",
"cs.HC"
] | This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e.g., keyboard and mouse operations). Unlike prevailing agent frameworks that depend on heavily wrapped commercial models (e.g., GPT-4o) with expert-crafted prompts and workflows,... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 1,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12327 | VARGPT: Unified Understanding and Generation in a Visual Autoregressive
Multimodal Large Language Model | [
"cs.CV"
] | We present VARGPT, a novel multimodal large language model (MLLM) that unifies visual understanding and generation within a single autoregressive framework. VARGPT employs a next-token prediction paradigm for visual understanding and a next-scale prediction paradigm for visual autoregressive generation. VARGPT innovati... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12330 | The Gap Between Principle and Practice of Lossy Image Coding | [
"cs.IT",
"cs.LG",
"math.IT"
] | Lossy image coding is the art of computing that is principally bounded by the image's rate-distortion function. This bound, though never accurately characterized, has been approached practically via deep learning technologies in recent years. Indeed, learned image coding schemes allow direct optimization of the joint r... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12331 | Cinepro: Robust Training of Foundation Models for Cancer Detection in
Prostate Ultrasound Cineloops | [
"eess.IV",
"cs.CV",
"cs.LG",
"q-bio.TO"
] | Prostate cancer (PCa) detection using deep learning (DL) models has shown potential for enhancing real-time guidance during biopsies. However, prostate ultrasound images lack pixel-level cancer annotations, introducing label noise. Current approaches often focus on limited regions of interest (ROIs), disregarding anato... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12332 | Automatic Labelling with Open-source LLMs using Dynamic Label Schema
Integration | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Acquiring labelled training data remains a costly task in real world machine learning projects to meet quantity and quality requirements. Recently Large Language Models (LLMs), notably GPT-4, have shown great promises in labelling data with high accuracy. However, privacy and cost concerns prevent the ubiquitous use of... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12336 | FuocChuVIP123 at CoMeDi Shared Task: Disagreement Ranking with
XLM-Roberta Sentence Embeddings and Deep Neural Regression | [
"cs.CL",
"cs.AI"
] | This paper presents results of our system for CoMeDi Shared Task, focusing on Subtask 2: Disagreement Ranking. Our system leverages sentence embeddings generated by the paraphrase-xlm-r-multilingual-v1 model, combined with a deep neural regression model incorporating batch normalization and dropout for improved general... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12337 | Understanding User Preference -- Comparison between Linear and
Directional Top-K Query results | [
"cs.DB"
] | This paper investigates user preferences for Linear Top-k Queries and Directional Top-k Queries, two methods for ranking results in multidimensional datasets. While Linear Queries prioritize weighted sums of attributes, Directional Queries aim to deliver more balanced results by incorporating the spatial relationship b... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 1,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12339 | Treefix: Enabling Execution with a Tree of Prefixes | [
"cs.SE",
"cs.AI"
] | The ability to execute code is a prerequisite for various dynamic program analyses. Learning-guided execution has been proposed as an approach to enable the execution of arbitrary code snippets by letting a neural model predict likely values for any missing variables. Although state-of-the-art learning-guided execution... | {
"Other": 1,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12344 | CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative
Fairness in Decentralized Learning | [
"cs.LG",
"cs.DC"
] | Collaborative learning (CL) enables multiple participants to jointly train machine learning (ML) models on decentralized data sources without raw data sharing. While the primary goal of CL is to maximize the expected accuracy gain for each participant, it is also important to ensure that the gains are fairly distribute... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12348 | Rate-Distortion-Perception Function of Bernoulli Vector Sources | [
"cs.IT",
"math.IT"
] | In this paper, we consider the rate-distortion-perception (RDP) trade-off for the lossy compression of a Bernoulli vector source, which is a finite collection of independent binary random variables. The RDP function quantifies in a way the efficient compression of a source when we impose a distortion constraint that li... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12349 | General Field Evaluation in High-Order Meshes on GPUs | [
"cs.MS",
"cs.CE"
] | Robust and scalable function evaluation at any arbitrary point in the finite/spectral element mesh is required for querying the partial differential equation solution at points of interest, comparison of solution between different meshes, and Lagrangian particle tracking. This is a challenging problem, particularly for... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 1,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12352 | Test-time regression: a unifying framework for designing sequence models
with associative memory | [
"cs.LG",
"cs.AI",
"cs.NE",
"stat.ML"
] | Sequences provide a remarkably general way to represent and process information. This powerful abstraction has placed sequence modeling at the center of modern deep learning applications, inspiring numerous architectures from transformers to recurrent networks. While this fragmented development has yielded powerful mod... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 1,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12354 | Diffusion-aware Censored Gaussian Processes for Demand Modelling | [
"cs.LG",
"stat.ME",
"stat.ML"
] | Inferring the true demand for a product or a service from aggregate data is often challenging due to the limited available supply, thus resulting in observations that are censored and correspond to the realized demand, thereby not accounting for the unsatisfied demand. Censored regression models are able to account for... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12356 | Vision-Language Models for Automated Chest X-ray Interpretation:
Leveraging ViT and GPT-2 | [
"cs.CV"
] | Radiology plays a pivotal role in modern medicine due to its non-invasive diagnostic capabilities. However, the manual generation of unstructured medical reports is time consuming and prone to errors. It creates a significant bottleneck in clinical workflows. Despite advancements in AI-generated radiology reports, chal... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12359 | Measured Hockey-Stick Divergence and its Applications to Quantum
Pufferfish Privacy | [
"quant-ph",
"cs.CR",
"cs.IT",
"cs.LG",
"math.IT"
] | The hockey-stick divergence is a fundamental quantity characterizing several statistical privacy frameworks that ensure privacy for classical and quantum data. In such quantum privacy frameworks, the adversary is allowed to perform all possible measurements. However, in practice, there are typically limitations to the ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 1,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12362 | ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse
Reinforcement Learning | [
"eess.SY",
"cs.SY"
] | Resilience of safety-critical systems is gaining importance, particularly with the increasing number of cyber and physical threats. Cyber-physical threats are becoming increasingly prevalent, as digital systems are ubiquitous in critical infrastructure. The challenge with determining the resilience of cyber-physical sy... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 1
} |
2501.12365 | Efficient Algorithm for Sparse Fourier Transform of Generalized q-ary
Functions | [
"cs.CC",
"cs.DM",
"cs.IT",
"cs.LG",
"math.IT"
] | Computing the Fourier transform of a $q$-ary function $f:\mathbb{Z}_{q}^n\rightarrow \mathbb{R}$, which maps $q$-ary sequences to real numbers, is an important problem in mathematics with wide-ranging applications in biology, signal processing, and machine learning. Previous studies have shown that, under the sparsity ... | {
"Other": 1,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12367 | Budget-constrained Collaborative Renewable Energy Forecasting Market | [
"cs.LG"
] | Accurate power forecasting from renewable energy sources (RES) is crucial for integrating additional RES capacity into the power system and realizing sustainability goals. This work emphasizes the importance of integrating decentralized spatio-temporal data into forecasting models. However, decentralized data ownership... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12368 | InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward
Model | [
"cs.CV",
"cs.CL"
] | Despite the promising performance of Large Vision Language Models (LVLMs) in visual understanding, they occasionally generate incorrect outputs. While reward models (RMs) with reinforcement learning or test-time scaling offer the potential for improving generation quality, a critical gap remains: publicly available mul... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12369 | DARB-Splatting: Generalizing Splatting with Decaying Anisotropic Radial
Basis Functions | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Splatting-based 3D reconstruction methods have gained popularity with the advent of 3D Gaussian Splatting, efficiently synthesizing high-quality novel views. These methods commonly resort to using exponential family functions, such as the Gaussian function, as reconstruction kernels due to their anisotropic nature, eas... | {
"Other": 1,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12370 | Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for
Mixture-of-Experts Language Models | [
"cs.LG",
"cs.AI"
] | Scaling the capacity of language models has consistently proven to be a reliable approach for improving performance and unlocking new capabilities. Capacity can be primarily defined by two dimensions: the number of model parameters and the compute per example. While scaling typically involves increasing both, the preci... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12371 | CAT and DOG: Improved Codes for Private Distributed Matrix
Multiplication | [
"cs.IT",
"math.IT"
] | We present novel constructions of polynomial codes for private distributed matrix multiplication (PDMM/SDMM) using outer product partitioning (OPP). We extend the degree table framework from the literature to cyclic-addition degree tables (CATs). By using roots of unity as evaluation points, we enable modulo-addition i... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12372 | Is Long Context All You Need? Leveraging LLM's Extended Context for
NL2SQL | [
"cs.DB",
"cs.AI"
] | Large Language Models (LLMs) have demonstrated impressive capabilities across a range of natural language processing tasks. In particular, improvements in reasoning abilities and the expansion of context windows have opened new avenues for leveraging these powerful models. NL2SQL is challenging in that the natural lang... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 1,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12374 | Expertise elevates AI usage: experimental evidence comparing laypeople
and professional artists | [
"cs.HC",
"cs.AI",
"cs.CY"
] | Novel capacities of generative AI to analyze and generate cultural artifacts raise inevitable questions about the nature and value of artistic education and human expertise. Has AI already leveled the playing field between professional artists and laypeople, or do trained artistic expressive capacity, curation skills a... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 1,
"cs.DB": 0,
"cs.HC": 1,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12375 | Video Depth Anything: Consistent Depth Estimation for Super-Long Videos | [
"cs.CV",
"cs.AI"
] | Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal inconsistency in videos, hindering its practical applications. Various methods have been proposed to alleviate this issue by leveraging video generation models or introducin... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12379 | Constant Weight Polar Codes through Periodic Markov Processes | [
"cs.IT",
"math.IT"
] | Constant weight codes can arise from an input process sampled from a periodic Markov chain. A previous result showed that, in general, polarization does not occur for input-output processes with an underlying periodic Markov chain. In this work, we show that if we fix the initial state of an underlying periodic Markov ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 1,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12380 | MMVU: Measuring Expert-Level Multi-Discipline Video Understanding | [
"cs.CV",
"cs.AI",
"cs.CL"
] | We introduce MMVU, a comprehensive expert-level, multi-discipline benchmark for evaluating foundation models in video understanding. MMVU includes 3,000 expert-annotated questions spanning 27 subjects across four core disciplines: Science, Healthcare, Humanities & Social Sciences, and Engineering. Compared to prior ben... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12381 | Parallel Sequence Modeling via Generalized Spatial Propagation Network | [
"cs.CV",
"cs.LG"
] | We present the Generalized Spatial Propagation Network (GSPN), a new attention mechanism optimized for vision tasks that inherently captures 2D spatial structures. Existing attention models, including transformers, linear attention, and state-space models like Mamba, process multi-dimensional data as 1D sequences, comp... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12382 | DiffDoctor: Diagnosing Image Diffusion Models Before Treating | [
"cs.CV"
] | In spite of the recent progress, image diffusion models still produce artifacts. A common solution is to refine an established model with a quality assessment system, which generally rates an image in its entirety. In this work, we believe problem-solving starts with identification, yielding the request that the model ... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12384 | CCESAR: Coastline Classification-Extraction From SAR Images Using
CNN-U-Net Combination | [
"cs.CV",
"cs.LG",
"eess.IV"
] | In this article, we improve the deep learning solution for coastline extraction from Synthetic Aperture Radar (SAR) images by proposing a two-stage model involving image classification followed by segmentation. We hypothesize that a single segmentation model usually used for coastline detection is insufficient to chara... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12385 | Audio Texture Manipulation by Exemplar-Based Analogy | [
"cs.SD",
"cs.LG",
"eess.AS"
] | Audio texture manipulation involves modifying the perceptual characteristics of a sound to achieve specific transformations, such as adding, removing, or replacing auditory elements. In this paper, we propose an exemplar-based analogy model for audio texture manipulation. Instead of conditioning on text-based instructi... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 1,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12386 | InternVideo2.5: Empowering Video MLLMs with Long and Rich Context
Modeling | [
"cs.CV"
] | This paper aims to improve the performance of video multimodal large language models (MLLM) via long and rich context (LRC) modeling. As a result, we develop a new version of InternVideo2.5 with a focus on enhancing the original MLLMs' ability to perceive fine-grained details and capture long-form temporal structure in... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12387 | Continuous 3D Perception Model with Persistent State | [
"cs.CV"
] | We present a unified framework capable of solving a broad range of 3D tasks. Our approach features a stateful recurrent model that continuously updates its state representation with each new observation. Given a stream of images, this evolving state can be used to generate metric-scale pointmaps (per-pixel 3D points) f... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12389 | Taming Teacher Forcing for Masked Autoregressive Video Generation | [
"cs.CV"
] | We introduce MAGI, a hybrid video generation framework that combines masked modeling for intra-frame generation with causal modeling for next-frame generation. Our key innovation, Complete Teacher Forcing (CTF), conditions masked frames on complete observation frames rather than masked ones (namely Masked Teacher Forci... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12390 | GPS as a Control Signal for Image Generation | [
"cs.CV"
] | We show that the GPS tags contained in photo metadata provide a useful control signal for image generation. We train GPS-to-image models and use them for tasks that require a fine-grained understanding of how images vary within a city. In particular, we train a diffusion model to generate images conditioned on both GPS... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12391 | Physics of Skill Learning | [
"cs.LG",
"cs.AI",
"physics.data-an",
"stat.ML"
] | We aim to understand physics of skill learning, i.e., how skills are learned in neural networks during training. We start by observing the Domino effect, i.e., skills are learned sequentially, and notably, some skills kick off learning right after others complete learning, similar to the sequential fall of domino cards... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12392 | Learning segmentation from point trajectories | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We consider the problem of segmenting objects in videos based on their motion and no other forms of supervision. Prior work has often approached this problem by using the principle of common fate, namely the fact that the motion of points that belong to the same object is strongly correlated. However, most authors have... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12393 | Towards Affordance-Aware Articulation Synthesis for Rigged Objects | [
"cs.CV"
] | Rigged objects are commonly used in artist pipelines, as they can flexibly adapt to different scenes and postures. However, articulating the rigs into realistic affordance-aware postures (e.g., following the context, respecting the physics and the personalities of the object) remains time-consuming and heavily relies o... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 1,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12394 | The ELEVATE-AI LLMs Framework: An Evaluation Framework for Use of Large
Language Models in HEOR: an ISPOR Working Group Report | [
"cs.CY",
"cs.LG"
] | Introduction. Generative Artificial Intelligence, particularly large language models (LLMs), offers transformative potential for Health Economics and Outcomes Research (HEOR). However, evaluating the quality, transparency, and rigor of LLM-assisted research lacks standardized guidance. This article introduces the ELEVA... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 1,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 0,
"cs.IT": 0,
"cs.LG": 1,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.12399 | FinSphere: A Conversational Stock Analysis Agent Equipped with
Quantitative Tools based on Real-Time Database | [
"cs.AI",
"cs.CL",
"cs.IR",
"q-fin.CP"
] | Current financial Large Language Models (LLMs) struggle with two critical limitations: a lack of depth in stock analysis, which impedes their ability to generate professional-grade insights, and the absence of objective evaluation metrics to assess the quality of stock analysis reports. To address these challenges, thi... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
"cs.CV": 0,
"cs.CY": 0,
"cs.DB": 0,
"cs.HC": 0,
"cs.IR": 1,
"cs.IT": 0,
"cs.LG": 0,
"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.