id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.15413 | FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing
Interpretability in Chest X-Ray Report Generation | [
"cs.CV",
"cs.AI"
] | Developing an interpretable system for generating reports in chest X-ray (CXR) analysis is becoming increasingly crucial in Computer-aided Diagnosis (CAD) systems, enabling radiologists to comprehend the decisions made by these systems. Despite the growth of diverse datasets and methods focusing on report generation, t... | {
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2411.15418 | Scaling Structure Aware Virtual Screening to Billions of Molecules with
SPRINT | [
"q-bio.BM",
"cs.LG"
] | Virtual screening of small molecules against protein targets can accelerate drug discovery and development by predicting drug-target interactions (DTIs). However, structure-based methods like molecular docking are too slow to allow for broad proteome-scale screens, limiting their application in screening for off-target... | {
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2411.15420 | Semi-supervised Single-view 3D Reconstruction via Multi Shape Prior
Fusion Strategy and Self-Attention | [
"cs.CV"
] | In the domain of single-view 3D reconstruction, traditional techniques have frequently relied on expensive and time-intensive 3D annotation data. Facing the challenge of annotation acquisition, semi-supervised learning strategies offer an innovative approach to reduce the dependence on labeled data. Despite these devel... | {
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2411.15421 | OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic
Surgical Video-Language Pretraining | [
"cs.CV"
] | Surgical practice involves complex visual interpretation, procedural skills, and advanced medical knowledge, making surgical vision-language pretraining (VLP) particularly challenging due to this complexity and the limited availability of annotated data. To address the gap, we propose OphCLIP, a hierarchical retrieval-... | {
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2411.15422 | Learning a local trading strategy: deep reinforcement learning for
grid-scale renewable energy integration | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY",
"math.OC"
] | Variable renewable generation increases the challenge of balancing power supply and demand. Grid-scale batteries co-located with generation can help mitigate this misalignment. This paper explores the use of reinforcement learning (RL) for operating grid-scale batteries co-located with solar power. Our results show RL ... | {
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2411.15426 | LDM-Morph: Latent diffusion model guided deformable image registration | [
"cs.CV"
] | Deformable image registration plays an essential role in various medical image tasks. Existing deep learning-based deformable registration frameworks primarily utilize convolutional neural networks (CNNs) or Transformers to learn features to predict the deformations. However, the lack of semantic information in the lea... | {
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2411.15428 | GeoAI-Enhanced Community Detection on Spatial Networks with Graph Deep
Learning | [
"cs.SI",
"cs.AI"
] | Spatial networks are useful for modeling geographic phenomena where spatial interaction plays an important role. To analyze the spatial networks and their internal structures, graph-based methods such as community detection have been widely used. Community detection aims to extract strongly connected components from th... | {
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2411.15430 | The Landscape of Data Reuse in Interactive Information Retrieval:
Motivations, Sources, and Evaluation of Reusability | [
"cs.IR",
"cs.DL"
] | Sharing and reusing research data can effectively reduce redundant efforts in data collection and curation, especially for small labs and research teams conducting human-centered system research, and enhance the replicability of evaluation experiments. Building a sustainable data reuse process and culture relies on fra... | {
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2411.15432 | Lifelong Knowledge Editing for Vision Language Models with Low-Rank
Mixture-of-Experts | [
"cs.CL",
"cs.CV"
] | Model editing aims to correct inaccurate knowledge, update outdated information, and incorporate new data into Large Language Models (LLMs) without the need for retraining. This task poses challenges in lifelong scenarios where edits must be continuously applied for real-world applications. While some editors demonstra... | {
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2411.15435 | What Makes a Scene ? Scene Graph-based Evaluation and Feedback for
Controllable Generation | [
"cs.CV"
] | While text-to-image generation has been extensively studied, generating images from scene graphs remains relatively underexplored, primarily due to challenges in accurately modeling spatial relationships and object interactions. To fill this gap, we introduce Scene-Bench, a comprehensive benchmark designed to evaluate ... | {
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2411.15436 | ConsistentAvatar: Learning to Diffuse Fully Consistent Talking Head
Avatar with Temporal Guidance | [
"cs.CV"
] | Diffusion models have shown impressive potential on talking head generation. While plausible appearance and talking effect are achieved, these methods still suffer from temporal, 3D or expression inconsistency due to the error accumulation and inherent limitation of single-image generation ability. In this paper, we pr... | {
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2411.15438 | Efficient Ternary Weight Embedding Model: Bridging Scalability and
Performance | [
"cs.CL"
] | Embedding models have become essential tools in both natural language processing and computer vision, enabling efficient semantic search, recommendation, clustering, and more. However, the high memory and computational demands of full-precision embeddings pose challenges for deployment in resource-constrained environme... | {
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2411.15439 | Twin Trigger Generative Networks for Backdoor Attacks against Object
Detection | [
"cs.CV"
] | Object detectors, which are widely used in real-world applications, are vulnerable to backdoor attacks. This vulnerability arises because many users rely on datasets or pre-trained models provided by third parties due to constraints on data and resources. However, most research on backdoor attacks has focused on image ... | {
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2411.15442 | Automatic High-quality Verilog Assertion Generation through
Subtask-Focused Fine-Tuned LLMs and Iterative Prompting | [
"cs.AR",
"cs.AI"
] | Formal Property Verification (FPV), using SystemVerilog Assertions (SVA), is crucial for ensuring the completeness of design with respect to the specification. However, writing SVA is a laborious task and has a steep learning curve. In this work, we present a large language model (LLM) -based flow to automatically gene... | {
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2411.15446 | freePruner: A Training-free Approach for Large Multimodal Model
Acceleration | [
"cs.CV",
"cs.AI"
] | Large Multimodal Models (LMMs) have demonstrated impressive capabilities in visual-language tasks but face significant deployment challenges due to their high computational demands. While recent token reduction methods show promise for accelerating LMMs, they typically require extensive retraining or fine-tuning, makin... | {
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2411.15447 | Gotta Hear Them All: Sound Source Aware Vision to Audio Generation | [
"cs.MM",
"cs.CV",
"cs.SD",
"eess.AS"
] | Vision-to-audio (V2A) synthesis has broad applications in multimedia. Recent advancements of V2A methods have made it possible to generate relevant audios from inputs of videos or still images. However, the immersiveness and expressiveness of the generation are limited. One possible problem is that existing methods sol... | {
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2411.15451 | Quantitative Analysis of IITs' Research Growth and SDG Contributions | [
"cs.IR"
] | The Indian Institutes of Technology (IITs) are vital to India's research ecosystem, advancing technology and engineering for industrial and societal benefits. This study reviews the research performance of top IITs-Bombay, Delhi, Madras, Kharagpur, and Kanpur based on Scopus-indexed publications (1952-2024). Research o... | {
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2411.15452 | Beyond inherent robustness: strong stability of MPC despite plant-model
mismatch | [
"eess.SY",
"cs.SY",
"math.OC"
] | We consider the asymptotic stability of MPC under plant-model mismatch, considering primarily models where the origin remains a steady state despite mismatch. Our results differ from prior results on the inherent robustness of MPC, which guarantee only convergence to a neighborhood of the origin, the size of which scal... | {
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2411.15453 | Enhancing Instruction-Following Capability of Visual-Language Models by
Reducing Image Redundancy | [
"cs.CV",
"cs.AI"
] | Large Language Models (LLMs) have strong instruction-following capability to interpret and execute tasks as directed by human commands. Multimodal Large Language Models (MLLMs) have inferior instruction-following ability compared to LLMs. However, there is a significant gap in the instruction-following capabilities bet... | {
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2411.15455 | MUFM: A Mamba-Enhanced Feedback Model for Micro Video Popularity
Prediction | [
"cs.MM",
"cs.AI"
] | The surge in micro-videos is transforming the concept of popularity. As researchers delve into vast multi-modal datasets, there is a growing interest in understanding the origins of this popularity and the forces driving its rapid expansion. Recent studies suggest that the virality of short videos is not only tied to t... | {
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2411.15457 | Hindi audio-video-Deepfake (HAV-DF): A Hindi language-based Audio-video
Deepfake Dataset | [
"cs.SD",
"cs.AI",
"cs.CR",
"cs.GR",
"cs.MM",
"eess.AS"
] | Deepfakes offer great potential for innovation and creativity, but they also pose significant risks to privacy, trust, and security. With a vast Hindi-speaking population, India is particularly vulnerable to deepfake-driven misinformation campaigns. Fake videos or speeches in Hindi can have an enormous impact on rural ... | {
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2411.15458 | TANGNN: a Concise, Scalable and Effective Graph Neural Networks with
Top-m Attention Mechanism for Graph Representation Learning | [
"cs.LG",
"cs.AI"
] | In the field of deep learning, Graph Neural Networks (GNNs) and Graph Transformer models, with their outstanding performance and flexible architectural designs, have become leading technologies for processing structured data, especially graph data. Traditional GNNs often face challenges in capturing information from di... | {
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2411.15459 | MambaVLT: Time-Evolving Multimodal State Space Model for Vision-Language
Tracking | [
"cs.CV"
] | The vision-language tracking task aims to perform object tracking based on various modality references. Existing Transformer-based vision-language tracking methods have made remarkable progress by leveraging the global modeling ability of self-attention. However, current approaches still face challenges in effectively ... | {
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2411.15462 | HateDay: Insights from a Global Hate Speech Dataset Representative of a
Day on Twitter | [
"cs.CL"
] | To tackle the global challenge of online hate speech, a large body of research has developed detection models to flag hate speech in the sea of online content. Yet, due to systematic biases in evaluation datasets, detection performance in real-world settings remains unclear, let alone across geographies. To address thi... | {
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2411.15466 | Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot
Subject-Driven Image Generator | [
"cs.CV"
] | Subject-driven text-to-image generation aims to produce images of a new subject within a desired context by accurately capturing both the visual characteristics of the subject and the semantic content of a text prompt. Traditional methods rely on time- and resource-intensive fine-tuning for subject alignment, while rec... | {
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2411.15468 | SplatSDF: Boosting Neural Implicit SDF via Gaussian Splatting Fusion | [
"cs.CV",
"cs.GR",
"cs.RO"
] | A signed distance function (SDF) is a useful representation for continuous-space geometry and many related operations, including rendering, collision checking, and mesh generation. Hence, reconstructing SDF from image observations accurately and efficiently is a fundamental problem. Recently, neural implicit SDF (SDF-N... | {
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2411.15469 | Mamba-CL: Optimizing Selective State Space Model in Null Space for
Continual Learning | [
"cs.CV"
] | Continual Learning (CL) aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge. Recently, State Space Models (SSMs), particularly the Mamba model, have achieved notable success in computer vision. Building on the strengths of SSMs, this study exp... | {
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2411.15470 | A Preliminary Study of Multilingual Code Language Models for Code
Generation Task Using Translated Benchmarks | [
"cs.SE",
"cs.AI",
"cs.PL"
] | Evaluating the performance of Code Language Models (CLMs) for software engineering tasks, especially in multilingual and low-resource programming language settings, poses significant challenges. These challenges are primarily due to the lack of high-quality benchmarks across various programming languages and the imbala... | {
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2411.15472 | KinMo: Kinematic-aware Human Motion Understanding and Generation | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Controlling human motion based on text presents an important challenge in computer vision. Traditional approaches often rely on holistic action descriptions for motion synthesis, which struggle to capture subtle movements of local body parts. This limitation restricts the ability to isolate and manipulate specific move... | {
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2411.15476 | Gassidy: Gaussian Splatting SLAM in Dynamic Environments | [
"cs.RO"
] | 3D Gaussian Splatting (3DGS) allows flexible adjustments to scene representation, enabling continuous optimization of scene quality during dense visual simultaneous localization and mapping (SLAM) in static environments. However, 3DGS faces challenges in handling environmental disturbances from dynamic objects with irr... | {
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2411.15477 | Towards Robust Evaluation of Unlearning in LLMs via Data Transformations | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vast corpus of texts from various sources; despite the best efforts during the data pre-processing stage while training the LLMs, they may pick... | {
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2411.15482 | SplatFlow: Self-Supervised Dynamic Gaussian Splatting in Neural Motion
Flow Field for Autonomous Driving | [
"cs.CV"
] | Most existing Dynamic Gaussian Splatting methods for complex dynamic urban scenarios rely on accurate object-level supervision from expensive manual labeling, limiting their scalability in real-world applications. In this paper, we introduce SplatFlow, a Self-Supervised Dynamic Gaussian Splatting within Neural Motion F... | {
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2411.15483 | Prob-cGAN: A Probabilistic Conditional Generative Adversarial Network
for LSD1 Inhibitor Activity Prediction | [
"cs.CE"
] | The inhibition of Lysine-Specific Histone Demethylase 1 (LSD1) is a promising strategy for cancer treatment and targeting epigenetic mechanisms. This paper introduces a Probabilistic Conditional Generative Adversarial Network (Prob-cGAN), designed to predict the activity of LSD1 inhibitors. The Prob-cGAN was evaluated ... | {
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2411.15484 | Seed-Free Synthetic Data Generation Framework for Instruction-Tuning
LLMs: A Case Study in Thai | [
"cs.CL"
] | We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We identify three key properties that contribute to the effectiveness of instruction-tuning datasets: fluency, diversity, and cultural context. W... | {
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2411.15486 | Transition Network Analysis: A Novel Framework for Modeling,
Visualizing, and Identifying the Temporal Patterns of Learners and Learning
Processes | [
"cs.SI",
"cs.CL",
"cs.CY"
] | This paper presents a novel learning analytics method: Transition Network Analysis (TNA), a method that integrates Stochastic Process Mining and probabilistic graph representation to model, visualize, and identify transition patterns in the learning process data. Combining the relational and temporal aspects into a sin... | {
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2411.15488 | Automatic Evaluation for Text-to-image Generation: Task-decomposed
Framework, Distilled Training, and Meta-evaluation Benchmark | [
"cs.CL",
"cs.AI",
"cs.CV"
] | Driven by the remarkable progress in diffusion models, text-to-image generation has made significant strides, creating a pressing demand for automatic quality evaluation of generated images. Current state-of-the-art automatic evaluation methods heavily rely on Multi-modal Large Language Models (MLLMs), particularly pow... | {
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2411.15490 | Improving Factuality of 3D Brain MRI Report Generation with Paired
Image-domain Retrieval and Text-domain Augmentation | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Acute ischemic stroke (AIS) requires time-critical management, with hours of delayed intervention leading to an irreversible disability of the patient. Since diffusion weighted imaging (DWI) using the magnetic resonance image (MRI) plays a crucial role in the detection of AIS, automated prediction of AIS from DWI has b... | {
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2411.15491 | Traditional Chinese Medicine Case Analysis System for High-Level
Semantic Abstraction: Optimized with Prompt and RAG | [
"cs.CL"
] | This paper details a technical plan for building a clinical case database for Traditional Chinese Medicine (TCM) using web scraping. Leveraging multiple platforms, including 360doc, we gathered over 5,000 TCM clinical cases, performed data cleaning, and structured the dataset with crucial fields such as patient details... | {
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2411.15494 | SilentWood: Private Inference Over Gradient-Boosting Decision Forests | [
"cs.CR",
"cs.DB"
] | Gradient-boosting decision forests, as used by algorithms such as XGBoost or AdaBoost, offer higher accuracy and lower training times for large datasets than decision trees. Protocols for private inference over decision trees can be used to preserve the privacy of the input data as well as the privacy of the trees. How... | {
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2411.15497 | AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven
Data Generation | [
"cs.CV"
] | Remote sensing image object detection (RSIOD) aims to identify and locate specific objects within satellite or aerial imagery. However, there is a scarcity of labeled data in current RSIOD datasets, which significantly limits the performance of current detection algorithms. Although existing techniques, e.g., data augm... | {
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2411.15500 | MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language
Model | [
"cs.ET",
"cs.CL"
] | Most current molecular language models transfer the masked language model or image-text generation model from natural language processing to molecular field. However, molecules are not solely characterized by atom/bond symbols; they encapsulate important physical/chemical properties. Moreover, normal language models br... | {
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2411.15501 | Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code
Snippet Adaptation Through Prompt Engineering | [
"cs.SE",
"cs.AI",
"cs.HC"
] | Code snippet adaptation is a fundamental activity in the software development process. Unlike code generation, code snippet adaptation is not a "free creation", which requires developers to tailor a given code snippet in order to fit specific requirements and the code context. Recently, large language models (LLMs) hav... | {
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2411.15504 | Effects of Muscle Synergy during Overhead Work with a Passive Shoulder
Exoskeleton: A Case Study | [
"physics.med-ph",
"cs.RO"
] | Objective: Shoulder exoskeletons can effectively assist with overhead work. However, their impacts on muscle synergy remain unclear. The objective is to systematically investigate the effects of the shoulder exoskeleton on muscle synergies during overhead work.Methods: Eight male participants were recruited to perform ... | {
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2411.15509 | Interactive Visual Assessment for Text-to-Image Generation Models | [
"cs.CV",
"cs.AI"
] | Visual generation models have achieved remarkable progress in computer graphics applications but still face significant challenges in real-world deployment. Current assessment approaches for visual generation tasks typically follow an isolated three-phase framework: test input collection, model output generation, and u... | {
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2411.15513 | SPA: Efficient User-Preference Alignment against Uncertainty in Medical
Image Segmentation | [
"eess.IV",
"cs.CV"
] | Medical image segmentation data inherently contain uncertainty, often stemming from both imperfect image quality and variability in labeling preferences on ambiguous pixels, which depend on annotators' expertise and the clinical context of the annotations. For instance, a boundary pixel might be labeled as tumor in dia... | {
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2411.15514 | CellPilot: A unified approach to automatic and interactive segmentation
in histopathology | [
"cs.CV"
] | Histopathology, the microscopic study of diseased tissue, is increasingly digitized, enabling improved visualization and streamlined workflows. An important task in histopathology is the segmentation of cells and glands, essential for determining shape and frequencies that can serve as indicators of disease. Deep learn... | {
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2411.15519 | Risk Management with Feature-Enriched Generative Adversarial Networks
(FE-GAN) | [
"q-fin.RM",
"cs.LG",
"stat.ML"
] | This paper investigates the application of Feature-Enriched Generative Adversarial Networks (FE-GAN) in financial risk management, with a focus on improving the estimation of Value at Risk (VaR) and Expected Shortfall (ES). FE-GAN enhances existing GANs architectures by incorporating an additional input sequence derive... | {
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2411.15523 | Enhancing Grammatical Error Detection using BERT with Cleaned Lang-8
Dataset | [
"cs.CL",
"cs.AI"
] | This paper presents an improved LLM based model for Grammatical Error Detection (GED), which is a very challenging and equally important problem for many applications. The traditional approach to GED involved hand-designed features, but recently, Neural Networks (NN) have automated the discovery of these features, impr... | {
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2411.15526 | Multi-scale Cascaded Large-Model for Whole-body ROI Segmentation | [
"eess.IV",
"cs.CV"
] | Organs-at-risk segmentation is critical for ensuring the safety and precision of radiotherapy and surgical procedures. However, existing methods for organs-at-risk image segmentation often suffer from uncertainties and biases in target selection, as well as insufficient model validation experiments, limiting their gene... | {
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2411.15527 | Haar-Laplacian for directed graphs | [
"cs.LG",
"cs.SI",
"stat.ML"
] | This paper introduces a novel Laplacian matrix aiming to enable the construction of spectral convolutional networks and to extend the signal processing applications for directed graphs. Our proposal is inspired by a Haar-like transformation and produces a Hermitian matrix which is not only in one-to-one relation with t... | {
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2411.15529 | Uplink Multiple Access with Heterogeneous Blocklength and Reliability
Constraints: Discrete Signaling with Treating Interference as Noise | [
"cs.IT",
"eess.SP",
"math.IT"
] | We consider the uplink multiple access of heterogeneous users, e.g., ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) users. Each user has its own reliability requirement and blocklength constraint, and users transmitting longer blocks suffer from heterogeneous interference. On top... | {
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2411.15530 | QEQR: An Exploration of Query Expansion Methods for Question Retrieval
in CQA Services | [
"cs.IR",
"cs.CL",
"cs.HC"
] | CQA services are valuable sources of knowledge that can be used to find answers to users' information needs. In these services, question retrieval aims to help users with their information needs by finding similar questions to theirs. However, finding similar questions is obstructed by the lexical gap that exists betwe... | {
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2411.15533 | Development of a Low-Cost Prosthetic Hand Using Electromyography and
Machine Learning | [
"cs.RO",
"cs.HC"
] | Electromyography (EMG) is a measure of muscular electrical activity and is used in many clinical/biomedical disciplines and modern human computer interaction. Myo-electric prosthetics analyze and classify the electrical signals recorded from the residual limb. The classified output is then used to control the position ... | {
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2411.15535 | Teaching Shortest Path Algorithms With a Robot and Overlaid Projections | [
"cs.RO",
"cs.HC"
] | Robots have the potential to enhance teaching of advanced computer science topics, making abstract concepts more tangible and interactive. In this paper, we present Timmy-a GoPiGo robot augmented with projections to demonstrate shortest path algorithms in an interactive learning environment. We integrated a JavaScript-... | {
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2411.15537 | MUNBa: Machine Unlearning via Nash Bargaining | [
"cs.CV"
] | Machine Unlearning (MU) aims to selectively erase harmful behaviors from models while retaining the overall utility of the model. As a multi-task learning problem, MU involves balancing objectives related to forgetting specific concepts/data and preserving general performance. A naive integration of these forgetting an... | {
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2411.15539 | Large Language Model with Region-guided Referring and Grounding for CT
Report Generation | [
"cs.CV",
"cs.AI"
] | Computed tomography (CT) report generation is crucial to assist radiologists in interpreting CT volumes, which can be time-consuming and labor-intensive. Existing methods primarily only consider the global features of the entire volume, making it struggle to focus on specific regions and potentially missing abnormaliti... | {
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2411.15540 | Optical-Flow Guided Prompt Optimization for Coherent Video Generation | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | While text-to-video diffusion models have made significant strides, many still face challenges in generating videos with temporal consistency. Within diffusion frameworks, guidance techniques have proven effective in enhancing output quality during inference; however, applying these methods to video diffusion models in... | {
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2411.15542 | Hierarchical Cross-Attention Network for Virtual Try-On | [
"cs.CV"
] | In this paper, we present an innovative solution for the challenges of the virtual try-on task: our novel Hierarchical Cross-Attention Network (HCANet). HCANet is crafted with two primary stages: geometric matching and try-on, each playing a crucial role in delivering realistic virtual try-on outcomes. A key feature of... | {
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2411.15548 | An unconditional distribution learning advantage with shallow quantum
circuits | [
"quant-ph",
"cs.AI"
] | One of the core challenges of research in quantum computing is concerned with the question whether quantum advantages can be found for near-term quantum circuits that have implications for practical applications. Motivated by this mindset, in this work, we prove an unconditional quantum advantage in the probably approx... | {
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2411.15550 | Class Order Disorder in Wikidata and First Fixes | [
"cs.IR",
"cs.AI"
] | Wikidata has a large ontology with classes at several orders. The Wikidata ontology has long been known to have violations of class order and information related to class order that appears suspect. SPARQL queries were evaluated against Wikidata to determine the prevalence of several kinds of violations and suspect inf... | {
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2411.15551 | NeRF Inpainting with Geometric Diffusion Prior and Balanced Score
Distillation | [
"cs.CV"
] | Recent advances in NeRF inpainting have leveraged pretrained diffusion models to enhance performance. However, these methods often yield suboptimal results due to their ineffective utilization of 2D diffusion priors. The limitations manifest in two critical aspects: the inadequate capture of geometric information by pr... | {
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2411.15553 | Improving Transferable Targeted Attacks with Feature Tuning Mixup | [
"cs.CV"
] | Deep neural networks exhibit vulnerability to adversarial examples that can transfer across different models. A particularly challenging problem is developing transferable targeted attacks that can mislead models into predicting specific target classes. While various methods have been proposed to enhance attack transfe... | {
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2411.15555 | Enhancing the Transferability of Adversarial Attacks on Face Recognition
with Diverse Parameters Augmentation | [
"cs.CV"
] | Face Recognition (FR) models are vulnerable to adversarial examples that subtly manipulate benign face images, underscoring the urgent need to improve the transferability of adversarial attacks in order to expose the blind spots of these systems. Existing adversarial attack methods often overlook the potential benefits... | {
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2411.15556 | ReWind: Understanding Long Videos with Instructed Learnable Memory | [
"cs.CV",
"cs.AI"
] | Vision-Language Models (VLMs) are crucial for applications requiring integrated understanding textual and visual information. However, existing VLMs struggle with long videos due to computational inefficiency, memory limitations, and difficulties in maintaining coherent understanding across extended sequences. To addre... | {
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2411.15557 | LAGUNA: LAnguage Guided UNsupervised Adaptation with structured spaces | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Unsupervised domain adaptation remains a critical challenge in enabling the knowledge transfer of models across unseen domains. Existing methods struggle to balance the need for domain-invariant representations with preserving domain-specific features, which is often due to alignment approaches that impose the projecti... | {
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2411.15558 | Reassessing Layer Pruning in LLMs: New Insights and Methods | [
"cs.LG",
"cs.CV"
] | Although large language models (LLMs) have achieved remarkable success across various domains, their considerable scale necessitates substantial computational resources, posing significant challenges for deployment in resource-constrained environments. Layer pruning, as a simple yet effective compression method, remove... | {
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2411.15560 | Do LLMs Agree on the Creativity Evaluation of Alternative Uses? | [
"cs.AI",
"cs.CL"
] | This paper investigates whether large language models (LLMs) show agreement in assessing creativity in responses to the Alternative Uses Test (AUT). While LLMs are increasingly used to evaluate creative content, previous studies have primarily focused on a single model assessing responses generated by the same model or... | {
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2411.15570 | Single Antenna Tracking and Localization of RIS-enabled Vehicular Users | [
"cs.IT",
"eess.SP",
"math.IT"
] | Reconfigurable Intelligent Surfaces (RISs) are envisioned to be employed in next generation wireless networks to enhance the communication and radio localization services. In this paper, we propose novel localization and tracking algorithms exploiting reflections through RISs at multiple receivers. We utilize a single ... | {
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2411.15576 | MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation
with Modality-Conditioned Text Embedding and Alternating Training | [
"eess.IV",
"cs.CV"
] | In the diverse field of medical imaging, automatic segmentation has numerous applications and must handle a wide variety of input domains, such as different types of Computed Tomography (CT) scans and Magnetic Resonance (MR) images. This heterogeneity challenges automatic segmentation algorithms to maintain consistent ... | {
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2411.15577 | From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive
Grammars | [
"cs.CL"
] | Recent advances in language modeling have demonstrated significant improvements in zero-shot capabilities, including in-context learning, instruction following, and machine translation for extremely under-resourced languages (Tanzer et al., 2024). However, many languages with limited written resources rely primarily on... | {
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2411.15580 | TKG-DM: Training-free Chroma Key Content Generation Diffusion Model | [
"cs.CV"
] | Diffusion models have enabled the generation of high-quality images with a strong focus on realism and textual fidelity. Yet, large-scale text-to-image models, such as Stable Diffusion, struggle to generate images where foreground objects are placed over a chroma key background, limiting their ability to separate foreg... | {
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2411.15582 | EMD: Explicit Motion Modeling for High-Quality Street Gaussian Splatting | [
"cs.CV"
] | Photorealistic reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. While recent methods based on 3D/4D Gaussian Splatting (GS) have demonstrated promising results, they still encounter challenges in complex street scenes due to the unpredictable motion of dynamic obj... | {
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2411.15584 | FLD+: Data-efficient Evaluation Metric for Generative Models | [
"cs.CV",
"cs.LG",
"eess.IV"
] | We introduce a new metric to assess the quality of generated images that is more reliable, data-efficient, compute-efficient, and adaptable to new domains than the previous metrics, such as Fr\'echet Inception Distance (FID). The proposed metric is based on normalizing flows, which allows for the computation of density... | {
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2411.15585 | Boosting Semi-Supervised Scene Text Recognition via Viewing and
Summarizing | [
"cs.CV"
] | Existing scene text recognition (STR) methods struggle to recognize challenging texts, especially for artistic and severely distorted characters. The limitation lies in the insufficient exploration of character morphologies, including the monotonousness of widely used synthetic training data and the sensitivity of the ... | {
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2411.15586 | Transparent but Powerful: Explainability, Accuracy, and Generalizability
in ADHD Detection from Social Media Data | [
"cs.CL"
] | Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental health condition affecting both children and adults, yet it remains severely underdiagnosed. Recent advances in artificial intelligence, particularly in Natural Language Processing (NLP) and Machine Learning (ML), offer promising solutions for scalab... | {
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2411.15589 | Deep Learning for THz Channel Estimation and Beamforming Prediction via
Sub-6GHz Channel | [
"eess.SP",
"cs.AI"
] | An efficient channel estimation is of vital importance to help THz communication systems achieve their full potential. Conventional uplink channel estimation methods, such as least square estimation, are practically inefficient for THz systems because of their large computation overhead. In this paper, we propose an ef... | {
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2411.15590 | From Complexity to Parsimony: Integrating Latent Class Analysis to
Uncover Multimodal Learning Patterns in Collaborative Learning | [
"cs.LG",
"cs.HC",
"stat.ME"
] | Multimodal Learning Analytics (MMLA) leverages advanced sensing technologies and artificial intelligence to capture complex learning processes, but integrating diverse data sources into cohesive insights remains challenging. This study introduces a novel methodology for integrating latent class analysis (LCA) within MM... | {
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2411.15592 | Classifier Enhanced Deep Learning Model for Erythroblast Differentiation
with Limited Data | [
"eess.IV",
"cs.CV"
] | Hematological disorders, which involve a variety of malignant conditions and genetic diseases affecting blood formation, present significant diagnostic challenges. One such major challenge in clinical settings is differentiating Erythroblast from WBCs. Our approach evaluates the efficacy of various machine learning (ML... | {
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2411.15594 | A Survey on LLM-as-a-Judge | [
"cs.CL",
"cs.AI"
] | Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale. Large Language Models (LLMs) have achieved remarkable success across diverse domains, leading to the emergence of "LLM-as-a-Judge," where LLMs... | {
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2411.15595 | An adversarial feature learning based semantic communication method for
Human 3D Reconstruction | [
"cs.CV",
"cs.AI"
] | With the widespread application of human body 3D reconstruction technology across various fields, the demands for data transmission and processing efficiency continue to rise, particularly in scenarios where network bandwidth is limited and low latency is required. This paper introduces an Adversarial Feature Learning-... | {
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2411.15596 | Comparative Analysis of Resource-Efficient CNN Architectures for Brain
Tumor Classification | [
"eess.IV",
"cs.CV"
] | Accurate brain tumor classification in MRI images is critical for timely diagnosis and treatment planning. While deep learning models like ResNet-18, VGG-16 have shown high accuracy, they often come with increased complexity and computational demands. This study presents a comparative analysis of effective yet simple C... | {
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2411.15600 | How Texts Help? A Fine-grained Evaluation to Reveal the Role of Language
in Vision-Language Tracking | [
"cs.CV",
"cs.AI"
] | Vision-language tracking (VLT) extends traditional single object tracking by incorporating textual information, providing semantic guidance to enhance tracking performance under challenging conditions like fast motion and deformations. However, current VLT trackers often underperform compared to single-modality methods... | {
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2411.15602 | Enhancing Object Detection Accuracy in Autonomous Vehicles Using
Synthetic Data | [
"cs.CV",
"cs.LG"
] | The rapid progress in machine learning models has significantly boosted the potential for real-world applications such as autonomous vehicles, disease diagnoses, and recognition of emergencies. The performance of many machine learning models depends on the nature and size of the training data sets. These models often f... | {
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2411.15604 | FATE: Full-head Gaussian Avatar with Textural Editing from Monocular
Video | [
"cs.CV"
] | Reconstructing high-fidelity, animatable 3D head avatars from effortlessly captured monocular videos is a pivotal yet formidable challenge. Although significant progress has been made in rendering performance and manipulation capabilities, notable challenges remain, including incomplete reconstruction and inefficient G... | {
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2411.15605 | GIFT: A Framework for Global Interpretable Faithful Textual Explanations
of Vision Classifiers | [
"cs.CV"
] | Understanding deep models is crucial for deploying them in safety-critical applications. We introduce GIFT, a framework for deriving post-hoc, global, interpretable, and faithful textual explanations for vision classifiers. GIFT starts from local faithful visual counterfactual explanations and employs (vision) language... | {
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2411.15611 | Knowledge Transfer Across Modalities with Natural Language Supervision | [
"cs.CV"
] | We present a way to learn novel concepts by only using their textual description. We call this method Knowledge Transfer. Similarly to human perception, we leverage cross-modal interaction to introduce new concepts. We hypothesize that in a pre-trained visual encoder there are enough low-level features already learned ... | {
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2411.15616 | A Scalable Approach to Covariate and Concept Drift Management via
Adaptive Data Segmentation | [
"cs.LG"
] | In many real-world applications, continuous machine learning (ML) systems are crucial but prone to data drift, a phenomenon where discrepancies between historical training data and future test data lead to significant performance degradation and operational inefficiencies. Traditional drift adaptation methods typically... | {
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2411.15617 | Non-Reciprocal Reconfigurable Intelligent Surfaces | [
"eess.SP",
"cs.SY",
"eess.SY"
] | In contrast to conventional RIS, the scattering matrix of a non-reciprocal RIS (NR-RIS) is non-symmetric, leading to differences in the uplink and the downlink components of NR-RIS cascaded channels. In this paper, a physically-consistent device model is proposed in which an NR-RIS is composed of multiple groups of two... | {
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2411.15618 | Accelerated Hydration Site Localization and Thermodynamic Profiling | [
"q-bio.BM",
"cs.LG"
] | Water plays a fundamental role in the structure and function of proteins and other biomolecules. The thermodynamic profile of water molecules surrounding a protein are critical for ligand binding and recognition. Therefore, identifying the location and thermodynamic behavior of relevant water molecules is important for... | {
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2411.15620 | Fine-Grained Open-Vocabulary Object Recognition via User-Guided
Segmentation | [
"cs.CV"
] | Recent advent of vision-based foundation models has enabled efficient and high-quality object detection at ease. Despite the success of previous studies, object detection models face limitations on capturing small components from holistic objects and taking user intention into account. To address these challenges, we p... | {
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2411.15621 | On the importance of local and global feature learning for automated
measurable residual disease detection in flow cytometry data | [
"cs.CV"
] | This paper evaluates various deep learning methods for measurable residual disease (MRD) detection in flow cytometry (FCM) data, addressing questions regarding the benefits of modeling long-range dependencies, methods of obtaining global information, and the importance of learning local features. Based on our findings,... | {
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} |
2411.15622 | Distributionally Robust Safety Verification for Markov Decision
Processes | [
"eess.SY",
"cs.SY"
] | In this paper, we propose a distributionally robust safety verification method for Markov decision processes where only an ambiguous transition kernel is available instead of the precise transition kernel. We define the ambiguity set around the nominal distribution by considering a Wasserstein distance. To this end, we... | {
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} |
2411.15623 | Multi-label Sequential Sentence Classification via Large Language Model | [
"cs.CL"
] | Sequential sentence classification (SSC) in scientific publications is crucial for supporting downstream tasks such as fine-grained information retrieval and extractive summarization. However, current SSC methods are constrained by model size, sequence length, and single-label setting. To address these limitations, thi... | {
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} |
2411.15624 | Trans-Glasso: A Transfer Learning Approach to Precision Matrix
Estimation | [
"stat.ML",
"cs.LG",
"stat.ME"
] | Precision matrix estimation is essential in various fields, yet it is challenging when samples for the target study are limited. Transfer learning can enhance estimation accuracy by leveraging data from related source studies. We propose Trans-Glasso, a two-step transfer learning method for precision matrix estimation.... | {
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} |
2411.15626 | Aligning Generalisation Between Humans and Machines | [
"cs.AI"
] | Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals. The responsible use of AI increasingly shows the need for human-AI teaming, necessitating effective intera... | {
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} |
2411.15628 | ACE: Action Concept Enhancement of Video-Language Models in Procedural
Videos | [
"cs.CV"
] | Vision-language models (VLMs) are capable of recognizing unseen actions. However, existing VLMs lack intrinsic understanding of procedural action concepts. Hence, they overfit to fixed labels and are not invariant to unseen action synonyms. To address this, we propose a simple fine-tuning technique, Action Concept Enha... | {
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} |
2411.15633 | Orthogonal Subspace Decomposition for Generalizable AI-Generated Image
Detection | [
"cs.CV"
] | AI-generated images (AIGIs), such as natural or face images, have become increasingly realistic and indistinguishable, making their detection a critical and pressing challenge. In this paper, we start from a new perspective to excavate the reason behind the failure generalization in AIGI detection, named the \textit{as... | {
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} |
2411.15634 | "All that Glitters": Approaches to Evaluations with Unreliable Model and
Human Annotations | [
"cs.CL",
"cs.AI",
"stat.AP"
] | "Gold" and "ground truth" human-mediated labels have error. The effects of this error can escape commonly reported metrics of label quality or obscure questions of accuracy, bias, fairness, and usefulness during model evaluation. This study demonstrates methods for answering such questions even in the context of very l... | {
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"cs.SY": 0
} |
2411.15638 | Learning state and proposal dynamics in state-space models using
differentiable particle filters and neural networks | [
"cs.LG",
"stat.CO",
"stat.ML"
] | State-space models are a popular statistical framework for analysing sequential data. Within this framework, particle filters are often used to perform inference on non-linear state-space models. We introduce a new method, StateMixNN, that uses a pair of neural networks to learn the proposal distribution and transition... | {
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} |
2411.15640 | AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering
Benchmark Dataset | [
"cs.CL"
] | Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. Particularly in low-and middle-income countries (LMICs) facing acute physician shortages and lack of specialists, LLMs offer a poten... | {
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"cs.SY": 0
} |
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