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Daily Arxiv
Daily Arxiv
世界中で発行される人工知能関連の論文をまとめるページです。
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論文の著作権は著者および関連機関にあり、共有する際は出典を明記してください。
VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI
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SingMOS-Pro: An Comprehensive Benchmark for Singing Quality Assessment
Pack and Force Your Memory: Long-form and Consistent Video Generation
Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
GPT and Prejudice: A Sparse Approach to Understanding Learned Representations in Large Language Models
Analyzing Latent Concepts in Code Language Models
Less is More: Lean yet Powerful Vision-Language Model for Autonomous Driving
DM-Bench: Benchmarking LLMs for Personalized Decision Making in Diabetes Management
YOLO-Based Defect Detection for Metal Sheets
Jina-reranker-v3: Last but Not Late Interaction for Listwise Document Reranking
SecInfer: Preventing Prompt Injection via Inference-time Scaling
Putnam-like dataset summary: LLMs as mathematical competition contestants
Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
Enhancing LLM Steering through Sparse Autoencoder-Based Vector Refinement
Observation-Free Attacks on Online Learning to Rank
MTRec: Learning to Align with User Preferences via Mental Reward Models
MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs
When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models
Flow-Induced Diagonal Gaussian Processes
Towards Size-invariant Salient Object Detection: A Generic Evaluation and Optimization Approach
Dual-Stage Reweighted MoE for Long-Tailed Egocentric Mistake Detection
Robust Pan-Cancer Mitotic Figure Detection with YOLOv12
Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMs
Better by Comparison: Retrieval-Augmented Contrastive Reasoning for Automatic Prompt Optimization
STORI: A Benchmark and Taxonomy for Stochastic Environments
A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI
Grounding the Ungrounded: A Spectral-Graph Framework for Quantifying Hallucinations in multimodal LLMs
FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering
RelayFormer: A Unified Local-Global Attention Framework for Scalable Image and Video Manipulation Localization
Quantum-RAG and PunGPT2: Advancing Low-Resource Language Generation and Retrieval for the Punjabi Language
Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective
SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes toward Intelligent Vehicle Suspension Systems
An Architecture for Spatial Networking
A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
First Hallucination Tokens Are Different from Conditional Ones
Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
Model Parallelism With Subnetwork Data Parallelism
VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
A Survey of Pun Generation: Datasets, Evaluations and Methodologies
Controlled Generation with Equivariant Variational Flow Matching
CAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree
DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation
SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration
Semantic Preprocessing for LLM-based Malware Analysis
Manipulating 3D Molecules in a Fixed-Dimensional E(3)-Equivariant Latent Space
Permissioned LLMs: Enforcing Access Control in Large Language Models
Efficient Preimage Approximation for Neural Network Certification
JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models
NeSyGeo: A Neuro-Symbolic Framework for Multimodal Geometric Reasoning Data Generation
Leveraging Online Data to Enhance Medical Knowledge in a Small Persian Language Model
Pre-training Limited Memory Language Models with Internal and External Knowledge
OT Score: An OT based Confidence Score for Source Free Unsupervised Domain Adaptation
Comparing Exploration-Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Experiments
A Survey of Deep Learning for Complex Speech Spectrograms
Continuous Thought Machines
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering
XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs
AlignDiT: Multimodal Aligned Diffusion Transformer for Synchronized Speech Generation
PropRAG: Guiding Retrieval with Beam Search over Proposition Paths
Activated LoRA: Fine-tuned LLMs for Intrinsics
Not a nuisance but a useful heuristic: Outlier dimensions favor frequent tokens in language models
Verbosity Tradeoffs and the Impact of Scale on the Faithfulness of LLM Self-Explanations
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement
DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation
A Multi-Fidelity Control Variate Approach for Policy Gradient Estimation
L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning
Rethinking the Vulnerability of Concept Erasure and a New Method
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Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM トレーニング
MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents
CBVLM: Training-free Explainable Concept-based Large Vision Language Models for Medical Image Classification
Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations
Inferring Pluggable Types with Machine Learning
Optimizing Container Loading and Unloading through Dual-Cycling and Dockyard Rehandle Reduction Using a Hybrid Genetic Algorithm
LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing
Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders
RACCooN: A Versatile Instructional Video Editing Framework with Auto-Generated Narratives
Unified Domain Adaptive Semantic Segmentation
Do AI Models Perform Human-like Abstract Reasoning Across Modalities?
Learning to Decide with Just Enough: Information-Theoretic Context Summarization for CMDPs
Thinkquel: A Model Dedicated to Text-to-dbt Using Synthetic Data and a Span-Aware Objective
OffTopicEval: When Large Language Models Enter the Wrong Chat, Almost Always!
Learning to Interact in World Latent for Team Coordination
Understanding Generative Recommendation with Semantic IDs from a Model-scaling View
GUI-PRA: Process Reward Agent for GUI Tasks
PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning
Efficient & Correct Predictive Equivalence for Decision Trees
THOR: Tool-Integrated Hierarchical Optimization via RL for Mathematical Reasoning
Gala: Global LLM Agents for Text-to-Model Translation
Disentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendation
LayerCake: Token-Aware Contrastive Decoding within Large Language Model Layers
Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems
V2X-UniPool: Unifying Multimodal Perception and Knowledge Reasoning for Autonomous Driving
MIRROR: Modular Internal Processing for Personalized Safety in LLM Dialogue
SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning
Grounding Multimodal LLMs to Embodied Agents that Ask for Help with Reinforcement Learning
ViLBias: Detecting and Reasoning about Bias in Multimodal Content
OML: A Primitive for Reconciling Open Access with Owner Control in AI Model Distribution
Improved Monte Carlo Planning via Causal Disentanglement for Structurally-Decomposed Markov Decision Processes
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Dual-Stage Reweighted MoE for Long-Tailed Egocentric Mistake Detection
Created by
Haebom
作者
Boyu Han, Qianqian Xu, Shilong Bao, Zhiyong Yang, Sicong Li, Qingming Huang
概要
本論文は、自己中心のビデオデータからユーザの不正確な行動を判断する問題を扱う。微妙で珍しいミスを処理するために、Dual-Stage Reweighted Mixture-of-Experts（DR-MoE）フレームワークを提案します。最初のステップでは、固定ViViTモデルとLoRAにチューニングされたViViTモデルを使用して特徴を抽出し、特徴レベルのエキスパートモジュールを介して組み合わせます。第2段階では、不均衡なクラス問題を軽減するための再重み付きクロスエントロピー、傾斜分布でのランキングを改善するためのAUC損失、および補正と一般化を改善するためのラベル認識損失とシャープネス認識最小化を使用して3つの分類器を訓練します。彼らの予測は分類レベルの専門家モジュールを使用して融合されます。提案された方法は、特にまれで曖昧な間違いを識別するための強力な性能を示す。
Takeaways、Limitations
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Takeaways：
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DR-MoEフレームワークは、自己中心のビデオデータ内のユーザーの不正確な行動を効果的に識別します。
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稀で曖昧な間違い事例の識別に強みを示す。
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ViViTモデルとLoRAチューニングを活用して特徴抽出の効率を高めた。
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さまざまな損失関数とエキスパートモジュールを使用してパフォーマンスを向上させました。
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(コード提供)
https://github.com/boyuh/DR-MoE
GitHub - boyuh/DR-MoE: This repository is the official code for the paper "Dual-Stage Reweighted MoE for Long-Tailed Egocentric Mistake Detection" (EgoVis HoloAssist Challenges @ CVPR 2025).
This repository is the official code for the paper "Dual-Stage Reweighted MoE for Long-Tailed Egocentric Mistake Detection" (EgoVis HoloAssist Challenges @ CVPR 2025). - boyuh/DR-MoE
github.com
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Limitations：
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具体的な実験結果とデータセットに関する情報の欠如。
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提案された方法の計算の複雑さに関する情報の欠如
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他の既存の方法との比較分析の欠如
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