id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.07518 | Hallucination Elimination and Semantic Enhancement Framework for
Vision-Language Models in Traffic Scenarios | [
"cs.CV"
] | Large vision-language models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding and generation tasks. However, these models occasionally generate hallucinatory texts, resulting in descriptions that seem reasonable but do not correspond to the image. This phenomenon can lead to wrong driving d... | {
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2412.07519 | Statistical Precoder Design in Multi-User Systems via Graph Neural
Networks and Generative Modeling | [
"cs.IT",
"eess.SP",
"math.IT"
] | This letter proposes a graph neural network (GNN)-based framework for statistical precoder design that leverages model-based insights to compactly represent statistical knowledge, resulting in efficient, lightweight architectures. The framework also supports approximate statistical information in frequency division dup... | {
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2412.07520 | Quantifying the Prediction Uncertainty of Machine Learning Models for
Individual Data | [
"cs.LG",
"cs.IT",
"math.IT"
] | Machine learning models have exhibited exceptional results in various domains. The most prevalent approach for learning is the empirical risk minimizer (ERM), which adapts the model's weights to reduce the loss on a training set and subsequently leverages these weights to predict the label for new test data. Nonetheles... | {
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2412.07521 | Knowledge-based model validation using a custom metric | [
"cs.CE"
] | Vehicle models have a long history of research and as of today are able to model the involved physics in a reasonable manner. However, each new vehicle has its new characteristics or parameters. The identification of these is the main task of an engineer. To validate whether the correct parameter set has been chosen is... | {
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2412.07526 | KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation | [
"eess.IV",
"cs.CV"
] | Knee osteoarthritis (OA) is the most common joint disorder and a leading cause of disability. Diagnosing OA severity typically requires expert assessment of X-ray images and is commonly based on the Kellgren-Lawrence grading system, a time-intensive process. This study aimed to develop an automated deep learning model ... | {
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2412.07527 | Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement
(JUDE) | [
"cs.CV"
] | Low-light and blurring issues are prevalent when capturing photos at night, often due to the use of long exposure to address dim environments. Addressing these joint problems can be challenging and error-prone if an end-to-end model is trained without incorporating an appropriate physical model. In this paper, we intro... | {
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2412.07534 | ReCap: Better Gaussian Relighting with Cross-Environment Captures | [
"cs.CV"
] | Accurate 3D objects relighting in diverse unseen environments is crucial for realistic virtual object placement. Due to the albedo-lighting ambiguity, existing methods often fall short in producing faithful relights. Without proper constraints, observed training views can be explained by numerous combinations of lighti... | {
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2412.07538 | Can Neural Decompilation Assist Vulnerability Prediction on Binary Code? | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Vulnerability prediction is valuable in identifying security issues more efficiently, even though it requires the source code of the target software system, which is a restrictive hypothesis. This paper presents an experimental study to predict vulnerabilities in binary code without source code or complex representatio... | {
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2412.07539 | Anomaly detection using Diffusion-based methods | [
"cs.LG"
] | This paper explores the utility of diffusion-based models for anomaly detection, focusing on their efficacy in identifying deviations in both compact and high-resolution datasets. Diffusion-based architectures, including Denoising Diffusion Probabilistic Models (DDPMs) and Diffusion Transformers (DiTs), are evaluated f... | {
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2412.07541 | A data-driven learned discretization approach in finite volume schemes
for hyperbolic conservation laws and varying boundary conditions | [
"math.NA",
"cs.AI",
"cs.NA"
] | This paper presents a data-driven finite volume method for solving 1D and 2D hyperbolic partial differential equations. This work builds upon the prior research incorporating a data-driven finite-difference approximation of smooth solutions of scalar conservation laws, where optimal coefficients of neural networks appr... | {
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2412.07544 | Contractive Dynamical Imitation Policies for Efficient Out-of-Sample
Recovery | [
"cs.LG",
"cs.RO",
"stat.ML"
] | Imitation learning is a data-driven approach to learning policies from expert behavior, but it is prone to unreliable outcomes in out-of-sample (OOS) regions. While previous research relying on stable dynamical systems guarantees convergence to a desired state, it often overlooks transient behavior. We propose a framew... | {
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2412.07545 | Fault Isolation for the Ink Deposition Process in High-End Industrial
Printers | [
"eess.SY",
"cs.SY"
] | This paper presents a mathematical framework for modeling the dynamic effects of three fault categories and six fault variants in the ink channels of high-end industrial printers. It also introduces a hybrid approach that combines model-based and data-based methods to detect and isolate these faults effectively. A key ... | {
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2412.07548 | Automatic Database Configuration Debugging using Retrieval-Augmented
Language Models | [
"cs.DB"
] | Database management system (DBMS) configuration debugging, e.g., diagnosing poorly configured DBMS knobs and generating troubleshooting recommendations, is crucial in optimizing DBMS performance. However, the configuration debugging process is tedious and, sometimes challenging, even for seasoned database administrator... | {
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2412.07556 | Optimization-Driven Design of Monolithic Soft-Rigid Grippers | [
"cs.RO",
"cs.MS"
] | Sim-to-real transfer remains a significant challenge in soft robotics due to the unpredictability introduced by common manufacturing processes such as 3D printing and molding. These processes often result in deviations from simulated designs, requiring multiple prototypes before achieving a functional system. In this s... | {
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2412.07559 | Adaptive Epsilon Adversarial Training for Robust Gravitational Wave
Parameter Estimation Using Normalizing Flows | [
"cs.LG"
] | Adversarial training with Normalizing Flow (NF) models is an emerging research area aimed at improving model robustness through adversarial samples. In this study, we focus on applying adversarial training to NF models for gravitational wave parameter estimation. We propose an adaptive epsilon method for Fast Gradient ... | {
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2412.07565 | Making the Flow Glow -- Robot Perception under Severe Lighting
Conditions using Normalizing Flow Gradients | [
"cs.CV"
] | Modern robotic perception is highly dependent on neural networks. It is well known that neural network-based perception can be unreliable in real-world deployment, especially in difficult imaging conditions. Out-of-distribution detection is commonly proposed as a solution for ensuring reliability in real-world deployme... | {
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2412.07567 | POMDP-Based Trajectory Planning for On-Ramp Highway Merging | [
"cs.RO"
] | This paper addresses the trajectory planning problem for automated vehicle on-ramp highway merging. To tackle this challenge, we extend our previous work on trajectory planning at unsignalized intersections using Partially Observable Markov Decision Processes (POMDPs). The method utilizes the Adaptive Belief Tree (ABT)... | {
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2412.07573 | Subtopic-aware View Sampling and Temporal Aggregation for Long-form
Document Matching | [
"cs.IR",
"cs.CL"
] | Long-form document matching aims to judge the relevance between two documents and has been applied to various scenarios. Most existing works utilize hierarchical or long context models to process documents, which achieve coarse understanding but may ignore details. Some researchers construct a document view with simila... | {
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2412.07579 | Unlocking the Potential of Reverse Distillation for Anomaly Detection | [
"cs.CV"
] | Knowledge Distillation (KD) is a promising approach for unsupervised Anomaly Detection (AD). However, the student network's over-generalization often diminishes the crucial representation differences between teacher and student in anomalous regions, leading to detection failures. To addresses this problem, the widely a... | {
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2412.07582 | Sequential In-Network Processing for Cell-Free Massive MIMO with
Capacity-Constrained Parallel Radio Stripes | [
"eess.SP",
"cs.IT",
"math.IT"
] | To ensure coherent signal processing across distributed Access Points (APs) in Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) systems, a fronthaul connection between the APs and a Central Processor (CP) is imperative. We consider a fronthaul network employing parallel radio stripes. In this system, APs are... | {
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2412.07583 | Mobile Video Diffusion | [
"cs.CV",
"cs.AI"
] | Video diffusion models have achieved impressive realism and controllability but are limited by high computational demands, restricting their use on mobile devices. This paper introduces the first mobile-optimized video diffusion model. Starting from a spatio-temporal UNet from Stable Video Diffusion (SVD), we reduce me... | {
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2412.07584 | Multimodal Contextualized Support for Enhancing Video Retrieval System | [
"cs.CV",
"cs.AI"
] | Current video retrieval systems, especially those used in competitions, primarily focus on querying individual keyframes or images rather than encoding an entire clip or video segment. However, queries often describe an action or event over a series of frames, not a specific image. This results in insufficient informat... | {
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2412.07585 | Scaling Sequential Recommendation Models with Transformers | [
"cs.LG",
"cs.AI"
] | Modeling user preferences has been mainly addressed by looking at users' interaction history with the different elements available in the system. Tailoring content to individual preferences based on historical data is the main goal of sequential recommendation. The nature of the problem, as well as the good performan... | {
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2412.07586 | Paired Wasserstein Autoencoders for Conditional Sampling | [
"cs.LG",
"stat.ML"
] | Wasserstein distances greatly influenced and coined various types of generative neural network models. Wasserstein autoencoders are particularly notable for their mathematical simplicity and straight-forward implementation. However, their adaptation to the conditional case displays theoretical difficulties. As a remedy... | {
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2412.07587 | A Hype-Adjusted Probability Measure for NLP Stock Return Forecasting | [
"q-fin.CP",
"cs.LG"
] | This article introduces a Hype-Adjusted Probability Measure in the context of a new Natural Language Processing (NLP) approach for stock return and volatility forecasting. A novel sentiment score equation is proposed to represent the impact of intraday news on forecasting next-period stock return and volatility for sel... | {
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2412.07589 | DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for
Customized Manga Generation | [
"cs.CV"
] | Story visualization, the task of creating visual narratives from textual descriptions, has seen progress with text-to-image generation models. However, these models often lack effective control over character appearances and interactions, particularly in multi-character scenes. To address these limitations, we propose ... | {
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2412.07590 | Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks
and Diffusion Model | [
"eess.IV",
"cs.CV"
] | Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. Removing motion artifacts is a straightforward solution and has been extensively studied. However, paired data are still heavily relied on in recent works and the perturbations in k-space (frequency domain) are... | {
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2412.07591 | CoinCLIP: A Multimodal Framework for Assessing Viability in Web3
Memecoins | [
"cs.CE"
] | The rapid growth of memecoins within the Web3 ecosystem, driven by platforms like Pump.fun, has made it easier for anyone to create tokens. However, this democratization has also led to an explosion of low-quality or bot-generated projects, often motivated by short-term financial gain. This overwhelming influx of specu... | {
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2412.07592 | Complexity of inversion of functions on the reals | [
"math.LO",
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"math.IT",
"math.PR"
] | We study the complexity of deterministic and probabilistic inversions of partial computable functions on the reals. | {
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2412.07594 | RFL: Simplifying Chemical Structure Recognition with Ring-Free Language | [
"cs.CV"
] | The primary objective of Optical Chemical Structure Recognition is to identify chemical structure images into corresponding markup sequences. However, the complex two-dimensional structures of molecules, particularly those with rings and multiple branches, present significant challenges for current end-to-end methods t... | {
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2412.07605 | Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph
Neural Networks | [
"cs.LG"
] | Graph Neural Networks (GNNs) demonstrate superior performance in various graph learning tasks, yet their wider real-world application is hindered by the computational overhead when applied to large-scale graphs. To address the issue, the Graph Lottery Hypothesis (GLT) has been proposed, advocating the identification of... | {
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2412.07608 | Faster and Better 3D Splatting via Group Training | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, demonstrating remarkable capability in high-fidelity scene reconstruction through its Gaussian primitive representations. However, the computational overhead induced by the massive number of primitives poses a significant bottlen... | {
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2412.07612 | ViewDelta: Text-Prompted Change Detection in Unaligned Images | [
"cs.CV"
] | Detecting changes between images is a fundamental problem in computer vision with broad applications in situational awareness, infrastructure assessment, environment monitoring, and industrial automation. Existing supervised models are typically limited to detecting specific types of changes, necessitating retraining f... | {
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2412.07616 | PVP: Polar Representation Boost for 3D Semantic Occupancy Prediction | [
"cs.CV"
] | Recently, polar coordinate-based representations have shown promise for 3D perceptual tasks. Compared to Cartesian methods, polar grids provide a viable alternative, offering better detail preservation in nearby spaces while covering larger areas. However, they face feature distortion due to non-uniform division. To ad... | {
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2412.07617 | Swarm Behavior Cloning | [
"cs.AI"
] | In sequential decision-making environments, the primary approaches for training agents are Reinforcement Learning (RL) and Imitation Learning (IL). Unlike RL, which relies on modeling a reward function, IL leverages expert demonstrations, where an expert policy $\pi_e$ (e.g., a human) provides the desired behavior. For... | {
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2412.07618 | Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced
Retrieval-Augmented Generation on Knowledge Graphs | [
"cs.AI",
"cs.CL"
] | Despite the superior performance of Large language models on many NLP tasks, they still face significant limitations in memorizing extensive world knowledge. Recent studies have demonstrated that leveraging the Retrieval-Augmented Generation (RAG) framework, combined with Knowledge Graphs that encapsulate extensive fac... | {
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2412.07619 | DRUM: Learning Demonstration Retriever for Large MUlti-modal Models | [
"cs.CL"
] | Recently, large language models (LLMs) have demonstrated impressive capabilities in dealing with new tasks with the help of in-context learning (ICL). In the study of Large Vision-Language Models (LVLMs), when implementing ICL, researchers usually adopts the naive strategies like fixed demonstrations across different s... | {
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2412.07626 | OmniDocBench: Benchmarking Diverse PDF Document Parsing with
Comprehensive Annotations | [
"cs.CV",
"cs.AI",
"cs.IR"
] | Document content extraction is crucial in computer vision, especially for meeting the high-quality data needs of large language models (LLMs) and retrieval-augmented generation (RAG) technologies. However, current document parsing methods suffer from significant limitations in terms of diversity and comprehensive evalu... | {
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2412.07629 | Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in
Table Question Answering | [
"cs.CL",
"cs.AI"
] | Applying language models (LMs) to tables is challenging due to the inherent structural differences between two-dimensional tables and one-dimensional text for which the LMs were originally designed. Furthermore, when applying linearized tables to LMs, the maximum token lengths often imposed in self-attention calculatio... | {
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2412.07633 | ChocoLlama: Lessons Learned From Teaching Llamas Dutch | [
"cs.CL"
] | While Large Language Models (LLMs) have shown remarkable capabilities in natural language understanding and generation, their performance often lags in lower-resource, non-English languages due to biases in the training data. In this work, we explore strategies for adapting the primarily English LLMs (Llama-2 and Llama... | {
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2412.07636 | TrojanWhisper: Evaluating Pre-trained LLMs to Detect and Localize
Hardware Trojans | [
"cs.CR",
"cs.AI"
] | Existing Hardware Trojans (HT) detection methods face several critical limitations: logic testing struggles with scalability and coverage for large designs, side-channel analysis requires golden reference chips, and formal verification methods suffer from state-space explosion. The emergence of Large Language Models (L... | {
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2412.07637 | Sampling from Boltzmann densities with physics informed low-rank formats | [
"cs.LG",
"math.OC"
] | Our method proposes the efficient generation of samples from an unnormalized Boltzmann density by solving the underlying continuity equation in the low-rank tensor train (TT) format. It is based on the annealing path commonly used in MCMC literature, which is given by the linear interpolation in the space of energies. ... | {
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2412.07638 | SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and
Several Attention Mechanisms | [
"cs.LG"
] | Many ensemble-based models have been proposed to solve machine learning problems in the survival analysis framework, including random survival forests, the gradient boosting machine with weak survival models, ensembles of the Cox models. To extend the set of models, a new ensemble-based model called SurvBETA (the Survi... | {
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2412.07639 | Offline Multi-Agent Reinforcement Learning via In-Sample Sequential
Policy Optimization | [
"cs.AI",
"cs.LG"
] | Offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn optimal multi-agent policies from pre-collected datasets. Compared to single-agent case, multi-agent setting involves a large joint state-action space and coupled behaviors of multiple agents, which bring extra complexity to offli... | {
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2412.07646 | Searching for Structure: Investigating Emergent Communication with Large
Language Models | [
"cs.CL"
] | Human languages have evolved to be structured through repeated language learning and use. These processes introduce biases that operate during language acquisition and shape linguistic systems toward communicative efficiency. In this paper, we investigate whether the same happens if artificial languages are optimised f... | {
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2412.07655 | Bayesian Data Augmentation and Training for Perception DNN in Autonomous
Aerial Vehicles | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Learning-based solutions have enabled incredible capabilities for autonomous systems. Autonomous vehicles, both aerial and ground, rely on DNN for various integral tasks, including perception. The efficacy of supervised learning solutions hinges on the quality of the training data. Discrepancies between training data a... | {
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2412.07658 | TraSCE: Trajectory Steering for Concept Erasure | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent advancements in text-to-image diffusion models have brought them to the public spotlight, becoming widely accessible and embraced by everyday users. However, these models have been shown to generate harmful content such as not-safe-for-work (NSFW) images. While approaches have been proposed to erase such abstrac... | {
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2412.07659 | Analytical-Heuristic Modeling and Optimization for Low-Light Image
Enhancement | [
"cs.CV"
] | Low-light image enhancement remains an open problem, and the new wave of artificial intelligence is at the center of this problem. This work describes the use of genetic algorithms for optimizing analytical models that can improve the visualization of images with poor light. Genetic algorithms are part of metaheuristic... | {
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2412.07660 | Proc-GS: Procedural Building Generation for City Assembly with 3D
Gaussians | [
"cs.CV"
] | Buildings are primary components of cities, often featuring repeated elements such as windows and doors. Traditional 3D building asset creation is labor-intensive and requires specialized skills to develop design rules. Recent generative models for building creation often overlook these patterns, leading to low visual ... | {
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2412.07664 | Dynamic Obstacle Avoidance of Unmanned Surface Vehicles in Maritime
Environments Using Gaussian Processes Based Motion Planning | [
"cs.RO"
] | During recent years, unmanned surface vehicles are extensively utilised in a variety of maritime applications such as the exploration of unknown areas, autonomous transportation, offshore patrol and others. In such maritime applications, unmanned surface vehicles executing relevant missions that might collide with pote... | {
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2412.07672 | FlexLLM: Exploring LLM Customization for Moving Target Defense on
Black-Box LLMs Against Jailbreak Attacks | [
"cs.CR",
"cs.CL"
] | Defense in large language models (LLMs) is crucial to counter the numerous attackers exploiting these systems to generate harmful content through manipulated prompts, known as jailbreak attacks. Although many defense strategies have been proposed, they often require access to the model's internal structure or need addi... | {
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2412.07674 | FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion
Models | [
"cs.CV"
] | Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications. However, accurately describing desired visual attributes can be challenging, especially for non-experts in art and photography. An intuitive solution involves adopting favorable attributes from the so... | {
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2412.07675 | RAZOR: Sharpening Knowledge by Cutting Bias with Unsupervised Text
Rewriting | [
"cs.CL",
"cs.LG"
] | Despite the widespread use of LLMs due to their superior performance in various tasks, their high computational costs often lead potential users to opt for the pretraining-finetuning pipeline. However, biases prevalent in manually constructed datasets can introduce spurious correlations between tokens and labels, creat... | {
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2412.07676 | BATIS: Bootstrapping, Autonomous Testing, and Initialization System for
Quantum Dot Devices | [
"cond-mat.mes-hall",
"cs.CV",
"cs.ET",
"quant-ph"
] | Semiconductor quantum dot (QD) devices have become central to advancements in spin-based quantum computing. As the complexity of QD devices grows, manual tuning becomes increasingly infeasible, necessitating robust and scalable autotuning solutions. Tuning large arrays of QD qubits depends on efficient choices of autom... | {
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2412.07678 | Can linguists better understand DNA? | [
"cs.CL",
"q-bio.GN"
] | Multilingual transfer ability, which reflects how well models fine-tuned on one source language can be applied to other languages, has been well studied in multilingual pre-trained models. However, the existence of such capability transfer between natural language and gene sequences/languages remains under explored.Thi... | {
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2412.07679 | RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models | [
"cs.CV",
"cs.AI"
] | Agglomerative models have recently emerged as a powerful approach to training vision foundation models, leveraging multi-teacher distillation from existing models such as CLIP, DINO, and SAM. This strategy enables the efficient creation of robust models, combining the strengths of individual teachers while significantl... | {
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2412.07682 | TRIM: Token Reduction and Inference Modeling for Cost-Effective Language
Generation | [
"cs.CL"
] | The inference cost of Large Language Models (LLMs) is a significant challenge due to their computational demands, specially on tasks requiring long outputs. However, natural language often contains redundancy, which presents an opportunity for optimization. We have observed that LLMs can generate distilled language-con... | {
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2412.07683 | RRT-GPMP2: A Motion Planner for Mobile Robots in Complex Maze
Environments | [
"cs.RO"
] | With the development of science and technology, mobile robots are playing a significant important role in the new round of world revolution. Further, mobile robots might assist or replace human beings in a great number of areas. To increase the degree of automation for mobile robots, advanced motion planners need to be... | {
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2412.07684 | The Pitfalls of Memorization: When Memorization Hurts Generalization | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explanations.This behavior leads to poor generalization when the learned explanations rely on spurious correlations. In this work, we formalize the interplay between memorization and gen... | {
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2412.07686 | Optimizing Sensor Redundancy in Sequential Decision-Making Problems | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Reinforcement Learning (RL) policies are designed to predict actions based on current observations to maximize cumulative future rewards. In real-world applications (i.e., non-simulated environments), sensors are essential for measuring the current state and providing the observations on which RL policies rely to make ... | {
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2412.07687 | Privacy-Preserving Customer Support: A Framework for Secure and Scalable
Interactions | [
"cs.LG",
"cs.CR",
"stat.AP",
"stat.ME",
"stat.ML"
] | The growing reliance on artificial intelligence (AI) in customer support has significantly improved operational efficiency and user experience. However, traditional machine learning (ML) approaches, which require extensive local training on sensitive datasets, pose substantial privacy risks and compliance challenges wi... | {
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2412.07688 | A Joint Energy and Differentially-Private Smart Meter Data Market | [
"eess.SY",
"cs.GT",
"cs.SY",
"econ.GN",
"q-fin.EC",
"q-fin.PM",
"q-fin.TR"
] | Given the vital role that smart meter data could play in handling uncertainty in energy markets, data markets have been proposed as a means to enable increased data access. However, most extant literature considers energy markets and data markets separately, which ignores the interdependence between them. In addition, ... | {
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2412.07689 | DriveMM: All-in-One Large Multimodal Model for Autonomous Driving | [
"cs.CV",
"cs.MM",
"cs.RO"
] | Large Multimodal Models (LMMs) have demonstrated exceptional comprehension and interpretation capabilities in Autonomous Driving (AD) by incorporating large language models. Despite the advancements, current data-driven AD approaches tend to concentrate on a single dataset and specific tasks, neglecting their overall c... | {
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2412.07693 | Leveraging Content and Context Cues for Low-Light Image Enhancement | [
"cs.CV",
"eess.IV"
] | Low-light conditions have an adverse impact on machine cognition, limiting the performance of computer vision systems in real life. Since low-light data is limited and difficult to annotate, we focus on image processing to enhance low-light images and improve the performance of any downstream task model, instead of fin... | {
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2412.07696 | SimVS: Simulating World Inconsistencies for Robust View Synthesis | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | Novel-view synthesis techniques achieve impressive results for static scenes but struggle when faced with the inconsistencies inherent to casual capture settings: varying illumination, scene motion, and other unintended effects that are difficult to model explicitly. We present an approach for leveraging generative vid... | {
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2412.07698 | Quantum vs. Classical Machine Learning Algorithms for Software Defect
Prediction: Challenges and Opportunities | [
"cs.SE",
"cs.LG"
] | Software defect prediction is a critical aspect of software quality assurance, as it enables early identification and mitigation of defects, thereby reducing the cost and impact of software failures. Over the past few years, quantum computing has risen as an exciting technology capable of transforming multiple domains;... | {
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2412.07704 | GEXIA: Granularity Expansion and Iterative Approximation for Scalable
Multi-grained Video-language Learning | [
"cs.CV"
] | In various video-language learning tasks, the challenge of achieving cross-modality alignment with multi-grained data persists. We propose a method to tackle this challenge from two crucial perspectives: data and modeling. Given the absence of a multi-grained video-text pretraining dataset, we introduce a Granularity E... | {
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2412.07713 | Benchmark for Evaluation and Analysis of Citation Recommendation Models | [
"cs.IR",
"cs.AI",
"cs.DL"
] | Citation recommendation systems have attracted much academic interest, resulting in many studies and implementations. These systems help authors automatically generate proper citations by suggesting relevant references based on the text they have written. However, the methods used in citation recommendation differ acro... | {
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2412.07720 | ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion
Transformer | [
"cs.CV"
] | The recent surge of interest in comprehensive multimodal models has necessitated the unification of diverse modalities. However, the unification suffers from disparate methodologies. Continuous visual generation necessitates the full-sequence diffusion-based approach, despite its divergence from the autoregressive mode... | {
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2412.07721 | ObjCtrl-2.5D: Training-free Object Control with Camera Poses | [
"cs.CV"
] | This study aims to achieve more precise and versatile object control in image-to-video (I2V) generation. Current methods typically represent the spatial movement of target objects with 2D trajectories, which often fail to capture user intention and frequently produce unnatural results. To enhance control, we present Ob... | {
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2412.07724 | Granite Guardian | [
"cs.CL"
] | We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with any large language model (LLM). These models offer comprehensive coverage across multiple risk dimensions, including social bias, profanity,... | {
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2412.07729 | Output-Sensitive Evaluation of Regular Path Queries | [
"cs.DB"
] | We study the classical evaluation problem for regular path queries: Given an edge-labeled graph and a regular path query, compute the set of pairs of vertices that are connected by paths that match the query. The Product Graph (PG) is the established evaluation approach for regular path queries. PG first constructs t... | {
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2412.07730 | STIV: Scalable Text and Image Conditioned Video Generation | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM"
] | The field of video generation has made remarkable advancements, yet there remains a pressing need for a clear, systematic recipe that can guide the development of robust and scalable models. In this work, we present a comprehensive study that systematically explores the interplay of model architectures, training recipe... | {
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2412.07736 | SKIPNet: Spatial Attention Skip Connections for Enhanced Brain Tumor
Classification | [
"eess.IV",
"cs.CV"
] | Early detection of brain tumors through magnetic resonance imaging (MRI) is essential for timely treatment, yet access to diagnostic facilities remains limited in remote areas. Gliomas, the most common primary brain tumors, arise from the carcinogenesis of glial cells in the brain and spinal cord, with glioblastoma pat... | {
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2412.07737 | Explainable machine learning for neoplasms diagnosis via
electrocardiograms: an externally validated study | [
"eess.SP",
"cs.LG"
] | Background: Neoplasms remains a leading cause of mortality worldwide, with timely diagnosis being crucial for improving patient outcomes. Current diagnostic methods are often invasive, costly, and inaccessible to many populations. Electrocardiogram (ECG) data, widely available and non-invasive, has the potential to ser... | {
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2412.07739 | GASP: Gaussian Avatars with Synthetic Priors | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Gaussian Splatting has changed the game for real-time photo-realistic rendering. One of the most popular applications of Gaussian Splatting is to create animatable avatars, known as Gaussian Avatars. Recent works have pushed the boundaries of quality and rendering efficiency but suffer from two main limitations. Either... | {
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2412.07741 | Image Retrieval with Intra-Sweep Representation Learning for Neck
Ultrasound Scanning Guidance | [
"cs.CV"
] | Purpose: Intraoperative ultrasound (US) can enhance real-time visualization in transoral robotic surgery. The surgeon creates a mental map with a pre-operative scan. Then, a surgical assistant performs freehand US scanning during the surgery while the surgeon operates at the remote surgical console. Communicating the t... | {
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2412.07743 | Zero-Shot ATC Coding with Large Language Models for Clinical Assessments | [
"cs.CL"
] | Manual assignment of Anatomical Therapeutic Chemical (ATC) codes to prescription records is a significant bottleneck in healthcare research and operations at Ontario Health and InterRAI Canada, requiring extensive expert time and effort. To automate this process while maintaining data privacy, we develop a practical ap... | {
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2412.07744 | StyleMaster: Stylize Your Video with Artistic Generation and Translation | [
"cs.CV"
] | Style control has been popular in video generation models. Existing methods often generate videos far from the given style, cause content leakage, and struggle to transfer one video to the desired style. Our first observation is that the style extraction stage matters, whereas existing methods emphasize global style bu... | {
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2412.07746 | LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation Models | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Emerging 3D geometric foundation models, such as DUSt3R, offer a promising approach for in-the-wild 3D vision tasks. However, due to the high-dimensional nature of the problem space and scarcity of high-quality 3D data, these pre-trained models still struggle to generalize to many challenging circumstances, such as lim... | {
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2412.07747 | Predictive Modeling of Homeless Service Assignment: A Representation
Learning Approach | [
"cs.LG",
"cs.AI"
] | In recent years, there has been growing interest in leveraging machine learning for homeless service assignment. However, the categorical nature of administrative data recorded for homeless individuals hinders the development of accurate machine learning methods for this task. This work asserts that deriving latent rep... | {
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2412.07750 | Multi-Shot Character Consistency for Text-to-Video Generation | [
"cs.CV"
] | Text-to-video models have made significant strides in generating short video clips from textual descriptions. Yet, a significant challenge remains: generating several video shots of the same characters, preserving their identity without hurting video quality, dynamics, and responsiveness to text prompts. We present Vid... | {
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2412.07751 | On Motion Blur and Deblurring in Visual Place Recognition | [
"cs.CV",
"eess.IV"
] | Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion b... | {
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2412.07752 | FlashRNN: Optimizing Traditional RNNs on Modern Hardware | [
"cs.LG",
"cs.AI"
] | While Transformers and other sequence-parallelizable neural network architectures seem like the current state of the art in sequence modeling, they specifically lack state-tracking capabilities. These are important for time-series tasks and logical reasoning. Traditional RNNs like LSTMs and GRUs, as well as modern vari... | {
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2412.07754 | PortraitTalk: Towards Customizable One-Shot Audio-to-Talking Face
Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Audio-driven talking face generation is a challenging task in digital communication. Despite significant progress in the area, most existing methods concentrate on audio-lip synchronization, often overlooking aspects such as visual quality, customization, and generalization that are crucial to producing realistic talki... | {
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2412.07755 | SAT: Spatial Aptitude Training for Multimodal Language Models | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.RO"
] | Spatial perception is a fundamental component of intelligence. While many studies highlight that large multimodal language models (MLMs) struggle to reason about space, they only test for static spatial reasoning, such as categorizing the relative positions of objects. Meanwhile, real-world deployment requires dynamic ... | {
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2412.07759 | 3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video
Generation | [
"cs.CV"
] | This paper aims to manipulate multi-entity 3D motions in video generation. Previous methods on controllable video generation primarily leverage 2D control signals to manipulate object motions and have achieved remarkable synthesis results. However, 2D control signals are inherently limited in expressing the 3D nature o... | {
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2412.07760 | SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse
Viewpoints | [
"cs.CV"
] | Recent advancements in video diffusion models have shown exceptional abilities in simulating real-world dynamics and maintaining 3D consistency. This progress inspires us to investigate the potential of these models to ensure dynamic consistency across various viewpoints, a highly desirable feature for applications suc... | {
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2412.07761 | Repurposing Pre-trained Video Diffusion Models for Event-based Video
Interpolation | [
"cs.CV"
] | Video Frame Interpolation aims to recover realistic missing frames between observed frames, generating a high-frame-rate video from a low-frame-rate video. However, without additional guidance, the large motion between frames makes this problem ill-posed. Event-based Video Frame Interpolation (EVFI) addresses this chal... | {
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2412.07762 | Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain
Offline Data | [
"cs.LG"
] | The modern paradigm in machine learning involves pre-training on diverse data, followed by task-specific fine-tuning. In reinforcement learning (RL), this translates to learning via offline RL on a diverse historical dataset, followed by rapid online RL fine-tuning using interaction data. Most RL fine-tuning methods re... | {
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2412.07763 | Bayesian Optimization of Antibodies Informed by a Generative Model of
Evolving Sequences | [
"stat.ML",
"cs.LG",
"q-bio.BM"
] | To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or by learning from large antibody databases to predict only typical antibodies. Unfortunately, the space of typical antibodies is enormous to s... | {
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2412.07766 | Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds | [
"cs.CV",
"cs.GR"
] | We present Make-A-Texture, a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively generates textures that are consistent across multiple viewpoints with a depth-aware inpainting diffusion model, in an optimized sequence of viewp... | {
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2412.07767 | Learning Visual Generative Priors without Text | [
"cs.CV"
] | Although text-to-image (T2I) models have recently thrived as visual generative priors, their reliance on high-quality text-image pairs makes scaling up expensive. We argue that grasping the cross-modality alignment is not a necessity for a sound visual generative prior, whose focus should be on texture modeling. Such a... | {
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} |
2412.07768 | Test-time Correction with Human Feedback: An Online 3D Detection System
via Visual Prompting | [
"cs.CV"
] | This paper introduces Test-time Correction (TTC) system, a novel online 3D detection system designated for online correction of test-time errors via human feedback, to guarantee the safety of deployed autonomous driving systems. Unlike well-studied offline 3D detectors frozen at inference, TTC explores the capability o... | {
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2412.07769 | BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities | [
"cs.CV"
] | This paper introduces BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model (LMM) with a unified architecture that integrates text and visual modalities, enabling advanced image understanding and medical applications. BiMediX2 leverages the Llama3.1 architecture and integrates text and visual... | {
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2412.07770 | From an Image to a Scene: Learning to Imagine the World from a Million
360 Videos | [
"cs.CV",
"cs.LG"
] | Three-dimensional (3D) understanding of objects and scenes play a key role in humans' ability to interact with the world and has been an active area of research in computer vision, graphics, and robotics. Large scale synthetic and object-centric 3D datasets have shown to be effective in training models that have 3D und... | {
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} |
2412.07771 | PETALface: Parameter Efficient Transfer Learning for Low-resolution Face
Recognition | [
"cs.CV"
] | Pre-training on large-scale datasets and utilizing margin-based loss functions have been highly successful in training models for high-resolution face recognition. However, these models struggle with low-resolution face datasets, in which the faces lack the facial attributes necessary for distinguishing different faces... | {
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2412.07772 | From Slow Bidirectional to Fast Autoregressive Video Diffusion Models | [
"cs.CV"
] | Current video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies. The generation of a single frame requires the model to process the entire sequence, including the future. We address this limitation by adapting a pretrained bidirec... | {
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2412.07773 | Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body
Control | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Humanoid robots require both robust lower-body locomotion and precise upper-body manipulation. While recent Reinforcement Learning (RL) approaches provide whole-body loco-manipulation policies, they lack precise manipulation with high DoF arms. In this paper, we propose decoupling upper-body control from locomotion, us... | {
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2412.07774 | UniReal: Universal Image Generation and Editing via Learning Real-world
Dynamics | [
"cs.CV"
] | We introduce UniReal, a unified framework designed to address various image generation and editing tasks. Existing solutions often vary by tasks, yet share fundamental principles: preserving consistency between inputs and outputs while capturing visual variations. Inspired by recent video generation models that effecti... | {
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} |
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