id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.04434 | Towards Real-Time Open-Vocabulary Video Instance Segmentation | [
"cs.CV"
] | In this paper, we address the challenge of performing open-vocabulary video instance segmentation (OV-VIS) in real-time. We analyze the computational bottlenecks of state-of-the-art foundation models that performs OV-VIS, and propose a new method, TROY-VIS, that significantly improves processing speed while maintaining... | {
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2412.04440 | GenMAC: Compositional Text-to-Video Generation with Multi-Agent
Collaboration | [
"cs.CV"
] | Text-to-video generation models have shown significant progress in the recent years. However, they still struggle with generating complex dynamic scenes based on compositional text prompts, such as attribute binding for multiple objects, temporal dynamics associated with different objects, and interactions between obje... | {
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2412.04441 | Learning Artistic Signatures: Symmetry Discovery and Style Transfer | [
"cs.CV"
] | Despite nearly a decade of literature on style transfer, there is no undisputed definition of artistic style. State-of-the-art models produce impressive results but are difficult to interpret since, without a coherent definition of style, the problem of style transfer is inherently ill-posed. Early work framed style-tr... | {
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2412.04445 | Moto: Latent Motion Token as the Bridging Language for Robot
Manipulation | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | Recent developments in Large Language Models pre-trained on extensive corpora have shown significant success in various natural language processing tasks with minimal fine-tuning. This success offers new promise for robotics, which has long been constrained by the high cost of action-labeled data. We ask: given the abu... | {
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2412.04446 | DiCoDe: Diffusion-Compressed Deep Tokens for Autoregressive Video
Generation with Language Models | [
"cs.CV"
] | Videos are inherently temporal sequences by their very nature. In this work, we explore the potential of modeling videos in a chronological and scalable manner with autoregressive (AR) language models, inspired by their success in natural language processing. We introduce DiCoDe, a novel approach that leverages Diffusi... | {
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2412.04447 | EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning
in Real-World Scenarios | [
"cs.AI",
"cs.CV"
] | The advent of Multimodal Large Language Models, leveraging the power of Large Language Models, has recently demonstrated superior multimodal understanding and reasoning abilities, heralding a new era for artificial general intelligence. However, achieving AGI necessitates more than just comprehension and reasoning. A c... | {
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2412.04448 | MEMO: Memory-Guided Diffusion for Expressive Talking Video Generation | [
"cs.CV"
] | Recent advances in video diffusion models have unlocked new potential for realistic audio-driven talking video generation. However, achieving seamless audio-lip synchronization, maintaining long-term identity consistency, and producing natural, audio-aligned expressions in generated talking videos remain significant ch... | {
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2412.04449 | p-MoD: Building Mixture-of-Depths MLLMs via Progressive Ratio Decay | [
"cs.CV",
"cs.CL"
] | Despite the remarkable performance of multimodal large language models (MLLMs) across diverse tasks, the substantial training and inference costs impede their advancement. The majority of computation stems from the overwhelming volume of vision tokens processed by the transformer decoder. In this paper, we propose to b... | {
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2412.04452 | Four-Plane Factorized Video Autoencoders | [
"cs.CV"
] | Latent variable generative models have emerged as powerful tools for generative tasks including image and video synthesis. These models are enabled by pretrained autoencoders that map high resolution data into a compressed lower dimensional latent space, where the generative models can subsequently be developed while r... | {
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2412.04453 | NaVILA: Legged Robot Vision-Language-Action Model for Navigation | [
"cs.RO",
"cs.CV"
] | This paper proposes to solve the problem of Vision-and-Language Navigation with legged robots, which not only provides a flexible way for humans to command but also allows the robot to navigate through more challenging and cluttered scenes. However, it is non-trivial to translate human language instructions all the way... | {
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2412.04454 | Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction | [
"cs.CL"
] | Graphical User Interfaces (GUIs) are critical to human-computer interaction, yet automating GUI tasks remains challenging due to the complexity and variability of visual environments. Existing approaches often rely on textual representations of GUIs, which introduce limitations in generalization, efficiency, and scalab... | {
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2412.04455 | Code-as-Monitor: Constraint-aware Visual Programming for Reactive and
Proactive Robotic Failure Detection | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | Automatic detection and prevention of open-set failures are crucial in closed-loop robotic systems. Recent studies often struggle to simultaneously identify unexpected failures reactively after they occur and prevent foreseeable ones proactively. To this end, we propose Code-as-Monitor (CaM), a novel paradigm leveragin... | {
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2412.04456 | HeatFormer: A Neural Optimizer for Multiview Human Mesh Recovery | [
"cs.CV"
] | We introduce a novel method for human shape and pose recovery that can fully leverage multiple static views. We target fixed-multiview people monitoring, including elderly care and safety monitoring, in which calibrated cameras can be installed at the corners of a room or an open space but whose configuration may vary ... | {
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2412.04457 | Monocular Dynamic Gaussian Splatting is Fast and Brittle but Smooth
Motion Helps | [
"cs.CV"
] | Gaussian splatting methods are emerging as a popular approach for converting multi-view image data into scene representations that allow view synthesis. In particular, there is interest in enabling view synthesis for dynamic scenes using only monocular input data -- an ill-posed and challenging problem. The fast pace o... | {
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2412.04458 | Cubify Anything: Scaling Indoor 3D Object Detection | [
"cs.CV"
] | We consider indoor 3D object detection with respect to a single RGB(-D) frame acquired from a commodity handheld device. We seek to significantly advance the status quo with respect to both data and modeling. First, we establish that existing datasets have significant limitations to scale, accuracy, and diversity of ob... | {
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2412.04459 | Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field
Rendering | [
"cs.CV",
"cs.GR"
] | We propose an efficient radiance field rendering algorithm that incorporates a rasterization process on sparse voxels without neural networks or 3D Gaussians. There are two key contributions coupled with the proposed system. The first is to render sparse voxels in the correct depth order along pixel rays by using dynam... | {
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2412.04460 | LayerFusion: Harmonized Multi-Layer Text-to-Image Generation with
Generative Priors | [
"cs.CV"
] | Large-scale diffusion models have achieved remarkable success in generating high-quality images from textual descriptions, gaining popularity across various applications. However, the generation of layered content, such as transparent images with foreground and background layers, remains an under-explored area. Layered... | {
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2412.04462 | 4Real-Video: Learning Generalizable Photo-Realistic 4D Video Diffusion | [
"cs.CV"
] | We propose 4Real-Video, a novel framework for generating 4D videos, organized as a grid of video frames with both time and viewpoint axes. In this grid, each row contains frames sharing the same timestep, while each column contains frames from the same viewpoint. We propose a novel two-stream architecture. One stream p... | {
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2412.04463 | MegaSaM: Accurate, Fast, and Robust Structure and Motion from Casual
Dynamic Videos | [
"cs.CV"
] | We present a system that allows for accurate, fast, and robust estimation of camera parameters and depth maps from casual monocular videos of dynamic scenes. Most conventional structure from motion and monocular SLAM techniques assume input videos that feature predominantly static scenes with large amounts of parallax.... | {
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2412.04464 | DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose
Reconstruction | [
"cs.CV"
] | The choice of data representation is a key factor in the success of deep learning in geometric tasks. For instance, DUSt3R has recently introduced the concept of viewpoint-invariant point maps, generalizing depth prediction, and showing that one can reduce all the key problems in the 3D reconstruction of static scenes ... | {
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2412.04465 | UnZipLoRA: Separating Content and Style from a Single Image | [
"cs.CV"
] | This paper introduces UnZipLoRA, a method for decomposing an image into its constituent subject and style, represented as two distinct LoRAs (Low-Rank Adaptations). Unlike existing personalization techniques that focus on either subject or style in isolation, or require separate training sets for each, UnZipLoRA disent... | {
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2412.04466 | User-item fairness tradeoffs in recommendations | [
"cs.IR",
"cs.CY"
] | In the basic recommendation paradigm, the most (predicted) relevant item is recommended to each user. This may result in some items receiving lower exposure than they "should"; to counter this, several algorithmic approaches have been developed to ensure item fairness. These approaches necessarily degrade recommendatio... | {
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2412.04467 | VisionZip: Longer is Better but Not Necessary in Vision Language Models | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Recent advancements in vision-language models have enhanced performance by increasing the length of visual tokens, making them much longer than text tokens and significantly raising computational costs. However, we observe that the visual tokens generated by popular vision encoders, such as CLIP and SigLIP, contain sig... | {
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2412.04468 | NVILA: Efficient Frontier Visual Language Models | [
"cs.CV"
] | Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a family of open VLMs designed to optimize both efficiency and accuracy. Building on top of VILA, we improve its model architecture by first ... | {
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2412.04469 | QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming
Free-viewpoint Videos | [
"cs.CV",
"cs.AI"
] | Online free-viewpoint video (FVV) streaming is a challenging problem, which is relatively under-explored. It requires incremental on-the-fly updates to a volumetric representation, fast training and rendering to satisfy real-time constraints and a small memory footprint for efficient transmission. If achieved, it can e... | {
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2412.04470 | Turbo3D: Ultra-fast Text-to-3D Generation | [
"cs.CV"
] | We present Turbo3D, an ultra-fast text-to-3D system capable of generating high-quality Gaussian splatting assets in under one second. Turbo3D employs a rapid 4-step, 4-view diffusion generator and an efficient feed-forward Gaussian reconstructor, both operating in latent space. The 4-step, 4-view generator is a student... | {
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2412.04471 | PaintScene4D: Consistent 4D Scene Generation from Text Prompts | [
"cs.CV",
"cs.AI"
] | Recent advances in diffusion models have revolutionized 2D and 3D content creation, yet generating photorealistic dynamic 4D scenes remains a significant challenge. Existing dynamic 4D generation methods typically rely on distilling knowledge from pre-trained 3D generative models, often fine-tuned on synthetic object d... | {
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2412.04472 | Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either
Stereo or Mono Fail | [
"cs.CV"
] | We introduce Stereo Anywhere, a novel stereo-matching framework that combines geometric constraints with robust priors from monocular depth Vision Foundation Models (VFMs). By elegantly coupling these complementary worlds through a dual-branch architecture, we seamlessly integrate stereo matching with learned contextua... | {
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2412.04473 | Take Package as Language: Anomaly Detection Using Transformer | [
"cs.CR",
"cs.AI"
] | Network data packet anomaly detection faces numerous challenges, including exploring new anomaly supervision signals, researching weakly supervised anomaly detection, and improving model interpretability. This paper proposes NIDS-GPT, a GPT-based causal language model for network intrusion detection. Unlike previous wo... | {
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2412.04474 | NSTRI Global Collaborative Research Data Platform | [
"cs.CY",
"cs.IR"
] | The National Strategic Technology Research Institute (NSTRI) Data Platform operated by Seoul National University Hospital (SNUH) addresses the challenge of accessing Korean healthcare data for international research. This platform provides secure access to pseudonymized Korean healthcare data while integrating internat... | {
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2412.04475 | Advancing Marine Heatwave Forecasts: An Integrated Deep Learning
Approach | [
"physics.ao-ph",
"cs.LG"
] | Marine heatwaves (MHWs), an extreme climate phenomenon, pose significant challenges to marine ecosystems and industries, with their frequency and intensity increasing due to climate change. This study introduces an integrated deep learning approach to forecast short-to-long-term MHWs on a global scale. The approach com... | {
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2412.04476 | The Moral Mind(s) of Large Language Models | [
"cs.CY",
"cs.AI"
] | As large language models (LLMs) become integrated into decision-making across various sectors, key questions arise: do they exhibit an emergent "moral mind" - a consistent set of moral principles guiding their ethical judgments - and is this reasoning uniform or diverse across models? To investigate this, we presented ... | {
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2412.04477 | Intelligent Tutors for Adult Learners: An Analysis of Needs and
Challenges | [
"cs.CY",
"cs.AI"
] | This work examines the sociotechnical factors that influence the adoption and usage of intelligent tutoring systems in self-directed learning contexts, focusing specifically on adult learners. The study is divided into two parts. First, we present Apprentice Tutors, a novel intelligent tutoring system designed to addre... | {
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2412.04478 | LibEvolutionEval: A Benchmark and Study for Version-Specific Code
Generation | [
"cs.SE",
"cs.AI"
] | Recent advancements in code completion models have primarily focused on local file contexts. However, these studies do not fully capture the complexity of real-world software development, which often requires the use of rapidly-evolving public libraries. To fill the gap, we introduce LibEvolutionEval, a detailed study ... | {
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2412.04482 | NLP Cluster Analysis of Common Core State Standards and NAEP Item
Specifications | [
"cs.CY",
"cs.AI",
"cs.CL"
] | Camilli (2024) proposed a methodology using natural language processing (NLP) to map the relationship of a set of content standards to item specifications. This study provided evidence that NLP can be used to improve the mapping process. As part of this investigation, the nominal classifications of standards and items ... | {
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2412.04483 | AI-powered Digital Framework for Personalized Economical Quality
Learning at Scale | [
"cs.CY",
"cs.AI",
"cs.LG"
] | The disparity in access to quality education is significant, both between developed and developing countries and within nations, regardless of their economic status. Socioeconomic barriers and rapid changes in the job market further intensify this issue, highlighting the need for innovative solutions that can deliver q... | {
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2412.04484 | Epinet for Content Cold Start | [
"cs.IR",
"cs.AI"
] | The exploding popularity of online content and its user base poses an evermore challenging matching problem for modern recommendation systems. Unlike other frontiers of machine learning such as natural language, recommendation systems are responsible for collecting their own data. Simply exploiting current knowledge ca... | {
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2412.04485 | EDA-Aware RTL Generation with Large Language Models | [
"cs.AR",
"cs.AI"
] | Large Language Models (LLMs) have become increasingly popular for generating RTL code. However, producing error-free RTL code in a zero-shot setting remains highly challenging for even state-of-the-art LLMs, often leading to issues that require manual, iterative refinement. This additional debugging process can dramati... | {
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2412.04486 | The Global AI Vibrancy Tool | [
"cs.CY",
"cs.AI"
] | This paper presents the latest version of the Global AI Vibrancy Tool (GVT), an interactive suite of visualizations designed to facilitate the comparison of AI vibrancy across 36 countries, using 42 indicators organized into 8 pillars. The tool offers customizable features that allow users to conduct in-depth country-l... | {
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2412.04487 | Coal Mine Safety Alert System: Refining BP Neural Network with Genetic
Algorithm Optimization | [
"cs.NE"
] | In response to the persistent safety challenges within coal mines, this study proposes a novel approach integrating a three-layer feedforward backpropagation artificial neural network with a genetic algorithm (GA-BP) for establishing a safety early warning system. Focused on a coal mine in Shandong, China, the model's ... | {
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2412.04488 | Optimizing Student Ability Assessment: A Hierarchy Constraint-Aware
Cognitive Diagnosis Framework for Educational Contexts | [
"cs.CY",
"cs.AI"
] | Cognitive diagnosis (CD) aims to reveal students' proficiency in specific knowledge concepts. With the increasing adoption of intelligent education applications, accurately assessing students' knowledge mastery has become an urgent challenge. Although existing cognitive diagnosis frameworks enhance diagnostic accuracy ... | {
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2412.04489 | Estimating Potential Demand and Customers' Perception of Service Value
in a Two-station Service System | [
"eess.SY",
"cs.SY",
"math.PR"
] | The potential demand in the market and customers' perception of service value are crucial factors in pricing strategies, resource allocation, and other operational decisions. However, this information is typically private and not readily accessible. In this paper, we analyze a service system operating across two statio... | {
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2412.04492 | Socio-Emotional Response Generation: A Human Evaluation Protocol for
LLM-Based Conversational Systems | [
"cs.CL",
"cs.AI",
"cs.HC",
"cs.SI"
] | Conversational systems are now capable of producing impressive and generally relevant responses. However, we have no visibility nor control of the socio-emotional strategies behind state-of-the-art Large Language Models (LLMs), which poses a problem in terms of their transparency and thus their trustworthiness for crit... | {
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2412.04494 | MAG-V: A Multi-Agent Framework for Synthetic Data Generation and
Verification | [
"cs.CL"
] | Extending the capabilities of Large Language Models (LLMs) with functions or tools for environment interaction has led to the emergence of the agent paradigm. In industry, training an LLM is not always feasible because of the scarcity of domain data, legal holds on proprietary customer data, rapidly changing business r... | {
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2412.04497 | Opportunities and Challenges of Large Language Models for Low-Resource
Languages in Humanities Research | [
"cs.CL",
"cs.AI"
] | Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity. Despite their significance, these languages face critical challenges, including data scarcity and technological limitations, which hinder their comprehensive study and preservation. Recent ... | {
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2412.04498 | Large Language Models in Politics and Democracy: A Comprehensive Survey | [
"cs.CL",
"cs.AI",
"cs.CY"
] | The advancement of generative AI, particularly large language models (LLMs), has a significant impact on politics and democracy, offering potential across various domains, including policymaking, political communication, analysis, and governance. This paper surveys the recent and potential applications of LLMs in polit... | {
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2412.04502 | Physics-informed Gaussian Processes as Linear Model Predictive
Controller | [
"math.OC",
"cs.LG",
"cs.SY",
"eess.SY"
] | We introduce a novel algorithm for controlling linear time invariant systems in a tracking problem. The controller is based on a Gaussian Process (GP) whose realizations satisfy a system of linear ordinary differential equations with constant coefficients. Control inputs for tracking are determined by conditioning the ... | {
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2412.04503 | A Primer on Large Language Models and their Limitations | [
"cs.CL",
"cs.AI"
] | This paper provides a primer on Large Language Models (LLMs) and identifies their strengths, limitations, applications and research directions. It is intended to be useful to those in academia and industry who are interested in gaining an understanding of the key LLM concepts and technologies, and in utilising this kno... | {
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2412.04504 | Multi-Bin Batching for Increasing LLM Inference Throughput | [
"cs.CL",
"cs.DC",
"cs.LG",
"cs.SY",
"eess.SY"
] | As large language models (LLMs) grow in popularity for their diverse capabilities, improving the efficiency of their inference systems has become increasingly critical. Batching LLM requests is a critical step in scheduling the inference jobs on servers (e.g. GPUs), enabling the system to maximize throughput by allowin... | {
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2412.04505 | Achieving Semantic Consistency: Contextualized Word Representations for
Political Text Analysis | [
"cs.CL",
"econ.GN",
"q-fin.EC"
] | Accurately interpreting words is vital in political science text analysis; some tasks require assuming semantic stability, while others aim to trace semantic shifts. Traditional static embeddings, like Word2Vec effectively capture long-term semantic changes but often lack stability in short-term contexts due to embeddi... | {
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2412.04506 | Arctic-Embed 2.0: Multilingual Retrieval Without Compromise | [
"cs.CL",
"cs.IR",
"cs.LG"
] | This paper presents the training methodology of Arctic-Embed 2.0, a set of open-source text embedding models built for accurate and efficient multilingual retrieval. While prior works have suffered from degraded English retrieval quality, Arctic-Embed 2.0 delivers competitive retrieval quality on multilingual and Engli... | {
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2412.04508 | Video Quality Assessment: A Comprehensive Survey | [
"eess.IV",
"cs.CV"
] | Video quality assessment (VQA) is an important processing task, aiming at predicting the quality of videos in a manner highly consistent with human judgments of perceived quality. Traditional VQA models based on natural image and/or video statistics, which are inspired both by models of projected images of the real wor... | {
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2412.04509 | Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm
Detection | [
"cs.CL"
] | Sarcasm detection is a significant challenge in sentiment analysis due to the nuanced and context-dependent nature of verbiage. We introduce Pragmatic Metacognitive Prompting (PMP) to improve the performance of Large Language Models (LLMs) in sarcasm detection, which leverages principles from pragmatics and reflection ... | {
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2412.04512 | Prompting Large Language Models for Clinical Temporal Relation
Extraction | [
"cs.CL",
"cs.LG"
] | Objective: This paper aims to prompt large language models (LLMs) for clinical temporal relation extraction (CTRE) in both few-shot and fully supervised settings. Materials and Methods: This study utilizes four LLMs: Encoder-based GatorTron-Base (345M)/Large (8.9B); Decoder-based LLaMA3-8B/MeLLaMA-13B. We developed ful... | {
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} |
2412.04521 | FedDW: Distilling Weights through Consistency Optimization in
Heterogeneous Federated Learning | [
"cs.LG",
"cs.CE"
] | Federated Learning (FL) is an innovative distributed machine learning paradigm that enables neural network training across devices without centralizing data. While this addresses issues of information sharing and data privacy, challenges arise from data heterogeneity across clients and increasing network scale, leading... | {
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2412.04523 | Labeling questions inside issue trackers | [
"cs.SE",
"cs.LG"
] | One of the issues faced by the maintainers of popular open source software is the triage of newly reported issues. Many of the issues submitted to issue trackers are questions. Many people ask questions on issue trackers about their problem instead of using a proper QA website like StackOverflow. This may seem insignif... | {
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2412.04526 | Leveraging Multimodal Protein Representations to Predict Protein Melting
Temperatures | [
"cs.LG",
"cs.CE"
] | Accurately predicting protein melting temperature changes (Delta Tm) is fundamental for assessing protein stability and guiding protein engineering. Leveraging multi-modal protein representations has shown great promise in capturing the complex relationships among protein sequences, structures, and functions. In this s... | {
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} |
2412.04531 | MageBench: Bridging Large Multimodal Models to Agents | [
"cs.CV",
"cs.AI",
"cs.LG"
] | LMMs have shown impressive visual understanding capabilities, with the potential to be applied in agents, which demand strong reasoning and planning abilities. Nevertheless, existing benchmarks mostly assess their reasoning abilities in language part, where the chain-of-thought is entirely composed of text.We consider ... | {
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2412.04532 | WinTSR: A Windowed Temporal Saliency Rescaling Method for Interpreting
Time Series Deep Learning Models | [
"cs.LG",
"cs.AI"
] | Interpreting complex time series forecasting models is challenging due to the temporal dependencies between time steps and the dynamic relevance of input features over time. Existing interpretation methods are limited by focusing mostly on classification tasks, evaluating using custom baseline models instead of the lat... | {
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2412.04533 | Mask-Adapter: The Devil is in the Masks for Open-Vocabulary Segmentation | [
"cs.CV"
] | Recent open-vocabulary segmentation methods adopt mask generators to predict segmentation masks and leverage pre-trained vision-language models, e.g., CLIP, to classify these masks via mask pooling. Although these approaches show promising results, it is counterintuitive that accurate masks often fail to yield accurate... | {
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2412.04534 | MoD-ART: Modal Decomposition of Acoustic Radiance Transfer | [
"cs.SD",
"cs.SY",
"eess.AS",
"eess.SY"
] | Modeling late reverberation at interactive speeds is a challenging task when multiple sound sources and listeners are present in the same environment. This is especially problematic when the environment is geometrically complex and/or features uneven energy absorption (e.g. coupled volumes), because in such cases the l... | {
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2412.04536 | Robotic Wire Arc Additive Manufacturing with Variable Height Layers | [
"cs.RO"
] | Robotic wire arc additive manufacturing has been widely adopted due to its high deposition rates and large print volume relative to other metal additive manufacturing processes. For complex geometries, printing with variable height within layers offer the advantage of producing overhangs without the need for support ma... | {
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2412.04537 | Understanding Hidden Computations in Chain-of-Thought Reasoning | [
"cs.CL",
"cs.LG"
] | Chain-of-Thought (CoT) prompting has significantly enhanced the reasoning abilities of large language models. However, recent studies have shown that models can still perform complex reasoning tasks even when the CoT is replaced with filler(hidden) characters (e.g., "..."), leaving open questions about how models inter... | {
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2412.04538 | Communication Compression for Distributed Learning without Control
Variates | [
"cs.LG",
"eess.SP",
"math.OC"
] | Distributed learning algorithms, such as the ones employed in Federated Learning (FL), require communication compression to reduce the cost of client uploads. The compression methods used in practice are often biased, which require error feedback to achieve convergence when the compression is aggressive. In turn, error... | {
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2412.04558 | Action-based image editing guided by human instructions | [
"cs.CV"
] | Text-based image editing is typically approached as a static task that involves operations such as inserting, deleting, or modifying elements of an input image based on human instructions. Given the static nature of this task, in this paper, we aim to make this task dynamic by incorporating actions. By doing this, we i... | {
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2412.04565 | Solving High-dimensional Inverse Problems Using Amortized
Likelihood-free Inference with Noisy and Incomplete Data | [
"cs.LG"
] | We present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observati... | {
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2412.04568 | Machine learning approach for mapping the stable orbits around planets | [
"astro-ph.EP",
"astro-ph.IM",
"cs.LG",
"physics.comp-ph"
] | Numerical N-body simulations are commonly used to explore stability regions around exoplanets, offering insights into the possible existence of satellites and ring systems. This study aims to utilize Machine Learning (ML) techniques to generate predictive maps of stable regions surrounding a hypothetical planet. The ap... | {
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2412.04571 | Dissociating Artificial Intelligence from Artificial Consciousness | [
"cs.AI",
"cs.CY",
"q-bio.NC"
] | Developments in machine learning and computing power suggest that artificial general intelligence is within reach. This raises the question of artificial consciousness: if a computer were to be functionally equivalent to a human, being able to do all we do, would it experience sights, sounds, and thoughts, as we do whe... | {
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2412.04573 | Give me Some Hard Questions: Synthetic Data Generation for Clinical QA | [
"cs.CL"
] | Clinical Question Answering (QA) systems enable doctors to quickly access patient information from electronic health records (EHRs). However, training these systems requires significant annotated data, which is limited due to the expertise needed and the privacy concerns associated with clinical data. This paper explor... | {
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2412.04576 | Show, Don't Tell: Uncovering Implicit Character Portrayal using LLMs | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Tools for analyzing character portrayal in fiction are valuable for writers and literary scholars in developing and interpreting compelling stories. Existing tools, such as visualization tools for analyzing fictional characters, primarily rely on explicit textual indicators of character attributes. However, portrayal i... | {
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2412.04577 | Data-Driven, Parameterized Reduced-order Models for Predicting
Distortion in Metal 3D Printing | [
"cs.LG",
"cs.CE"
] | In Laser Powder Bed Fusion (LPBF), the applied laser energy produces high thermal gradients that lead to unacceptable final part distortion. Accurate distortion prediction is essential for optimizing the 3D printing process and manufacturing a part that meets geometric accuracy requirements. This study introduces data-... | {
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2412.04578 | Loss Terms and Operator Forms of Koopman Autoencoders | [
"cs.LG",
"physics.comp-ph"
] | Koopman autoencoders are a prevalent architecture in operator learning. But, the loss functions and the form of the operator vary significantly in the literature. This paper presents a fair and systemic study of these options. Furthermore, it introduces novel loss terms. | {
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2412.04580 | ARTeFACT: Benchmarking Segmentation Models on Diverse Analogue Media
Damage | [
"cs.CV"
] | Accurately detecting and classifying damage in analogue media such as paintings, photographs, textiles, mosaics, and frescoes is essential for cultural heritage preservation. While machine learning models excel in correcting degradation if the damage operator is known a priori, we show that they fail to robustly predic... | {
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2412.04584 | The relevance of higher-order ties | [
"physics.soc-ph",
"cs.SI"
] | Higher-order networks effectively represent complex systems with group interactions. Existing methods usually overlook the relative contribution of group interactions (hyperlinks) of different sizes to the overall network structure. Yet, this has many important applications, especially when the network has meaningful n... | {
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2412.04591 | MetaFormer: High-fidelity Metalens Imaging via Aberration Correcting
Transformers | [
"eess.IV",
"cs.CV"
] | Metalens is an emerging optical system with an irreplaceable merit in that it can be manufactured in ultra-thin and compact sizes, which shows great promise of various applications such as medical imaging and augmented/virtual reality (AR/VR). Despite its advantage in miniaturization, its practicality is constrained by... | {
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2412.04592 | EgoPoints: Advancing Point Tracking for Egocentric Videos | [
"cs.CV"
] | We introduce EgoPoints, a benchmark for point tracking in egocentric videos. We annotate 4.7K challenging tracks in egocentric sequences. Compared to the popular TAP-Vid-DAVIS evaluation benchmark, we include 9x more points that go out-of-view and 59x more points that require re-identification (ReID) after returning to... | {
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2412.04594 | Learning Symmetries via Weight-Sharing with Doubly Stochastic Tensors | [
"cs.LG",
"cs.CV"
] | Group equivariance has emerged as a valuable inductive bias in deep learning, enhancing generalization, data efficiency, and robustness. Classically, group equivariant methods require the groups of interest to be known beforehand, which may not be realistic for real-world data. Additionally, baking in fixed group equiv... | {
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2412.04596 | Nonlinear Operator Learning Using Energy Minimization and MLPs | [
"cs.LG",
"cs.NA",
"math.NA"
] | We develop and evaluate a method for learning solution operators to nonlinear problems governed by partial differential equations. The approach is based on a finite element discretization and aims at representing the solution operator by an MLP that takes latent variables as input. The latent variables will typically c... | {
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2412.04602 | Formulation of probability theory problem with subtle condition | [
"cs.CL",
"math.PR"
] | Problems in probability theory prove to be one of the most challenging for students. Here, we formulate and discuss four related problems in probability theory that proved difficult for first to fourth-year undergraduate students whose first language was not English. These examples emphasize how crucial it is to unders... | {
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2412.04604 | ARC Prize 2024: Technical Report | [
"cs.AI"
] | As of December 2024, the ARC-AGI benchmark is five years old and remains unbeaten. We believe it is currently the most important unsolved AI benchmark in the world because it seeks to measure generalization on novel tasks -- the essence of intelligence -- as opposed to skill at tasks that can be prepared for in advance... | {
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2412.04606 | Semantic Consistency-Based Uncertainty Quantification for Factuality in
Radiology Report Generation | [
"cs.AI",
"cs.CL"
] | Radiology report generation (RRG) has shown great potential in assisting radiologists by automating the labor-intensive task of report writing. While recent advancements have improved the quality and coherence of generated reports, ensuring their factual correctness remains a critical challenge. Although generative med... | {
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2412.04610 | Exploring Transformer-Based Music Overpainting for Jazz Piano Variations | [
"cs.SD",
"cs.LG",
"eess.AS"
] | This paper explores transformer-based models for music overpainting, focusing on jazz piano variations. Music overpainting generates new variations while preserving the melodic and harmonic structure of the input. Existing approaches are limited by small datasets, restricting scalability and diversity. We introduce VAR... | {
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2412.04614 | Extractive Structures Learned in Pretraining Enable Generalization on
Finetuned Facts | [
"cs.LG",
"cs.CL"
] | Pretrained language models (LMs) can generalize to implications of facts that they are finetuned on. For example, if finetuned on ``John Doe lives in Tokyo," LMs can correctly answer ``What language do the people in John Doe's city speak?'' with ``Japanese''. However, little is known about the mechanisms that enable th... | {
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2412.04616 | Assessing and Learning Alignment of Unimodal Vision and Language Models | [
"cs.CV"
] | How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment methods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to ... | {
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2412.04619 | Sometimes I am a Tree: Data Drives Unstable Hierarchical Generalization | [
"cs.LG",
"cs.CL"
] | Language models (LMs), like other neural networks, often favor shortcut heuristics based on surface-level patterns. Although LMs behave like n-gram models early in training, they must eventually learn hierarchical syntactic representations to correctly apply grammatical rules out-of-distribution (OOD). In this work, we... | {
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2412.04620 | A CAV-based perimeter-free regional traffic control strategy utilizing
existing parking infrastructure | [
"eess.SY",
"cs.SY",
"math.OC"
] | This paper proposes a novel perimeter-free regional traffic management strategy for traffic networks under a connected and autonomous vehicle (CAV) environment. The proposed strategy requires CAVs, especially those with long remaining travel distances, to temporarily wait at nearby parking facilities when the network i... | {
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2412.04622 | Mixed Delay/Nondelay Embeddings Based Neuromorphic Computing with
Patterned Nanomagnet Arrays | [
"cs.ET",
"cs.LG"
] | Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previous works have demonstrated methods to physically probe these magnetization dynamics of PNAs to realize neuromorphic reservoir systems that ex... | {
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2412.04623 | Using Diffusion Priors for Video Amodal Segmentation | [
"cs.CV"
] | Object permanence in humans is a fundamental cue that helps in understanding persistence of objects, even when they are fully occluded in the scene. Present day methods in object segmentation do not account for this amodal nature of the world, and only work for segmentation of visible or modal objects. Few amodal metho... | {
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2412.04626 | BigDocs: An Open and Permissively-Licensed Dataset for Training
Multimodal Models on Document and Code Tasks | [
"cs.LG",
"cs.CL"
] | Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use ... | {
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2412.04628 | SWEPO: Simultaneous Weighted Preference Optimization for Group
Contrastive Alignment | [
"cs.LG",
"cs.AI",
"cs.CL"
] | We introduce Simultaneous Weighted Preference Optimization (SWEPO), a novel extension of Direct Preference Optimization (DPO) designed to accommodate multiple dynamically chosen positive and negative responses for each query. SWEPO employs a weighted group contrastive loss, assigning weights to responses based on their... | {
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2412.04629 | Argumentative Experience: Reducing Confirmation Bias on Controversial
Issues through LLM-Generated Multi-Persona Debates | [
"cs.HC",
"cs.CY",
"cs.IR"
] | Large language models (LLMs) are enabling designers to give life to exciting new user experiences for information access. In this work, we present a system that generates LLM personas to debate a topic of interest from different perspectives. How might information seekers use and benefit from such a system? Can centeri... | {
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2412.04634 | Neural Two-Level Monte Carlo Real-Time Rendering | [
"cs.GR",
"cs.AI"
] | We introduce an efficient Two-Level Monte Carlo (subset of Multi-Level Monte Carlo, MLMC) estimator for real-time rendering of scenes with global illumination. Using MLMC we split the shading integral into two parts: the radiance cache integral and the residual error integral that compensates for the bias of the first ... | {
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} |
2412.04637 | Semantic Retrieval at Walmart | [
"cs.IR",
"cs.AI",
"cs.LG"
] | In product search, the retrieval of candidate products before re-ranking is more critical and challenging than other search like web search, especially for tail queries, which have a complex and specific search intent. In this paper, we present a hybrid system for e-commerce search deployed at Walmart that combines tra... | {
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} |
2412.04639 | Motion-Guided Deep Image Prior for Cardiac MRI | [
"physics.med-ph",
"cs.CV",
"eess.IV"
] | Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. Traditional breath-held imaging protocols, however, pose challenges for patients with arrhythmias or limited breath-holding capacity. We introduce Motion-Guided Deep Image prior (M-DIP), a novel unsuper... | {
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} |
2412.04641 | Disentangled Representation Learning for Causal Inference with
Instruments | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Latent confounders are a fundamental challenge for inferring causal effects from observational data. The instrumental variable (IV) approach is a practical way to address this challenge. Existing IV based estimators need a known IV or other strong assumptions, such as the existence of two or more IVs in the system, whi... | {
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} |
2412.04642 | Improving LLM Group Fairness on Tabular Data via In-Context Learning | [
"cs.LG",
"cs.AI"
] | Large language models (LLMs) have been shown to be effective on tabular prediction tasks in the low-data regime, leveraging their internal knowledge and ability to learn from instructions and examples. However, LLMs can fail to generate predictions that satisfy group fairness, that is, produce equitable outcomes across... | {
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} |
2412.04645 | REL: Working out is all you need | [
"cs.AI"
] | Recent developments, particularly OpenAI's O1 model, have demonstrated the remarkable potential of Large Language Models (LLMs) for complex reasoning tasks. Through analysis of O1's outputs and provided sample Chain-of-Thought (CoT) demonstrations, we observe that it approaches problem-solving in a distinctly human-lik... | {
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} |
2412.04649 | Generating Whole-Body Avoidance Motion through Localized Proximity
Sensing | [
"cs.RO"
] | This paper presents a novel control algorithm for robotic manipulators in unstructured environments using proximity sensors partially distributed on the platform. The proposed approach exploits arrays of multi zone Time-of-Flight (ToF) sensors to generate a sparse point cloud representation of the robot surroundings. B... | {
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} |
2412.04650 | One Communication Round is All It Needs for Federated Fine-Tuning
Foundation Models | [
"cs.LG",
"cs.DC"
] | The recent advancement of large foundation models (FMs) has increased the demand for fine-tuning these models on large-scale and cross-domain datasets. To address this, federated fine-tuning has emerged as a solution, allowing models to be fine-tuned on distributed datasets across multiple devices while ensuring data p... | {
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} |
2412.04652 | Cross-Self KV Cache Pruning for Efficient Vision-Language Inference | [
"cs.CV",
"cs.AI"
] | KV cache pruning has emerged as a promising technique for reducing memory and computation costs in long-context auto-regressive generation. Existing methods for vision-language models (VLMs) typically rely on self-attention scores from large language models (LLMs) to identify and prune irrelevant tokens. However, these... | {
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} |
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