Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

Power Stabilization for AI Training Datacenters

A Systematic Study of Deep Learning Models and xAI Methods for Region-of-Interest Detection in MRI Scans

Documenting Deployment with Fabric: A Repository of Real-World AI Governance

Surya: Foundation Model for Heliophysics

Hard Examples Are All You Need: Maximizing GRPO Post-Training Under Annotation Budgets

MCLPD:Multi-view Contrastive Learning for EEG-based PD Detection Across Datasets

FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering

VerilogLAVD: LLM-Aided Rule Generation for Vulnerability Detection in Verilog

Kourkoutas-Beta: A Sunspike-Driven Adam Optimizer with Desert Flair

SecFSM: Knowledge Graph-Guided Verilog Code Generation for Secure Finite State Machines in Systems-on-Chip

Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats

LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients

Preacher: Paper-to-Video Agentic System

Agoran: An Agentic Open Marketplace for 6G RAN Automation

Architectural Co-Design for Zero-Shot Anomaly Detection: Decoupling Representation and Dynamically Fusing Features in CLIP

IBPS: Indian Bail Prediction System

Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time

TS-Insight: Visualizing Thompson Sampling for Verification and XAI

When Better Eyes Lead to Blindness: A Diagnostic Study of the Information Bottleneck in CNN-LSTM Image Captioning Models

Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters

Generation of structure-guided pMHC-I libraries using Diffusion Models

Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients

MCA-RG: Enhancing LLMs with Medical Concept Alignment for Radiology Report Generation

KEA Explain: Explanations of Hallucinations using Graph Kernel Analysis

Empirical Evidence for Alignment Faking in a Small LLM and Prompt-Based Mitigation Techniques

A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis

Deep regularization networks for inverse problems with noisy operators

LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles

On the Fundamental Impossibility of Hallucination Control in Large Language Models

Lossless Token Sequence Compression via Meta-Tokens

Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer

Flexible Tool Selection through Low-dimensional Attribute Alignment of Vision and Language

Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model

MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning

Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey

Sadeed: Advancing Arabic Diacritization Through Small Language Model

Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs

CaRL: Learning Scalable Planning Policies with Simple Rewards

On the Consistency of GNN Explanations for Malware Detection

Cequel: Cost-Effective Querying of Large Language Models for Text Clustering

Kuwain 1.5B: An Arabic SLM via Language Injection

MuSeD: A Multimodal Spanish Dataset for Sexism Detection in Social Media Videos

TextSplat: Text-Guided Semantic Fusion for Generalizable Gaussian Splatting

VerifiAgent: a Unified Verification Agent in Language Model Reasoning

Embodied Long Horizon Manipulation with Closed-loop Code Generation and Incremental Few-shot Adaptation

Revisiting Out-of-Distribution Detection in Real-time Object Detection: From Benchmark Pitfalls to a New Mitigation Paradigm

A Case for Specialisation in Non-Human Entities

Pragmatic Inference Chain (PIC) Improving LLMs' Reasoning of Authentic Implicit Toxic Language

Synthetic vs. Gold: The Role of LLM Generated Labels and Data in Cyberbullying Detection

Innamark: A Whitespace Replacement Information-Hiding Method

Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering

RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation

Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users

Self-Supervised Prompt Optimization

Learning to Generate Unit Tests for Automated Debugging

Modeling Discrimination with Causal Abstraction

Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition

Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models

Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review

Fine-tuning foundational models to code diagnoses from veterinary health records

Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs

Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models

Continual Learning for Multimodal Data Fusion of a Soft Gripper

BoostTrack++: using tracklet information to detect more objects in multiple object tracking

OPDR: Order-Preserving Dimension Reduction for Semantic Embedding of Multimodal Scientific Data

CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains

Generating 3D Terrain with 2D Cellular Automata

Unplug and Play Language Models: Decomposing Experts in Language Models at Inference Time

Using a cognitive architecture to consider antiBlackness in design and development of AI systems

ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings

ThinkTuning: Instilling Cognitive Reflections without Distillation

A "good regulator theorem" for embodied agents

Prescriptive Agents based on RAG for Automated Maintenance (PARAM)

One Subgoal at a Time: Zero-Shot Generalization to Arbitrary Linear Temporal Logic Requirements in Multi-Task Reinforcement Learning

Opus: A Prompt Intention Framework for Complex Workflow Generation

Exploring Big Five Personality and AI Capability Effects in LLM-Simulated Negotiation Dialogues

It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics

GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy

Automatic Curriculum Design for Zero-Shot Human-AI Coordination

PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data

SycEval: Evaluating LLM Sycophancy

CopyrightShield: Enhancing Diffusion Model Security against Copyright Infringement Attacks

VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making

Exploring the Effect of Explanation Content and Format on User Comprehension and Trust in Healthcare

On Learning Action Costs from Input Plans

Human-Object Interaction from Human-Level Instructions

Non-linear Welfare-Aware Strategic Learning

CRISPR-GPT for Agentic Automation of Gene-editing Experiments

SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass

Discovering Hidden Algebraic Structures via Transformers with Rank-Aware Beam GRPO

LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries

Neural Robot Dynamics

Dissecting Tool-Integrated Reasoning: An Empirical Study and Analysis

"Does the cafe entrance look accessible? Where is the door?" Towards Geospatial AI Agents for Visual Inquiries

End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning

Numerical models outperform AI weather forecasts of record-breaking extremes

EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-Commerce Models

Tutorial on the Probabilistic Unification of Estimation Theory, Machine Learning, and Generative AI

StreamMem: Query-Agnostic KV Cache Memory for Streaming Video Understanding

BoostTrack++: using tracklet information to detect more objects in multiple object tracking

Created by

Haebom

저자

Vuka\v{s}in Stanojevic, Branimir Todorovic

개요

본 논문은 다중 객체 추적(MOT)에서의 신뢰도 높은 검출 바운딩 박스 선택 문제에 초점을 맞춥니다. 기존의 BoostTrack이 검출 신뢰도 향상을 통해 다단계 연결 방식의 단점을 극복하려는 시도를 했지만, 본 논문은 BoostTrack의 신뢰도 향상 기법의 한계를 지적하고 성능 개선을 위한 새로운 방법을 제안합니다. 제안된 방법은 형태, 마할라노비스 거리, 그리고 새로운 소프트 BIoU 유사도를 결합하여 더욱 풍부한 유사도 측정을 구축하고 진짜 양성 검출을 더 잘 선택할 수 있도록 합니다. 또한, 유사도 측정값과 이전 신뢰도 점수를 기반으로 새로운 신뢰도 점수를 계산하는 소프트 검출 신뢰도 향상 기법과, 불규칙적으로 업데이트되는 검출과 트랙릿 간의 낮은 유사도 측정값을 고려하기 위한 가변 유사도 임계값을 도입합니다. 제안된 추가 사항들은 상호 독립적이며, 어떤 MOT 알고리즘에도 적용 가능합니다. 결과적으로, BoostTrack+ 기준에 제안된 방법을 결합하여 MOT17 데이터셋에서 최첨단 성능에 근접한 결과를, MOT20 데이터셋에서 HOTA 및 IDF1 점수에 대한 새로운 최첨단 결과를 달성합니다. 소스 코드는 제공됩니다.

시사점, 한계점

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시사점:

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다중 객체 추적(MOT)에서의 신뢰도 높은 검출 바운딩 박스 선택 문제에 대한 효과적인 해결책 제시.

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형태, 마할라노비스 거리, 소프트 BIoU 유사도를 결합한 새로운 유사도 측정 방식 제안.

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소프트 검출 신뢰도 향상 기법과 가변 유사도 임계값 도입을 통한 성능 향상.

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MOT17 및 MOT20 데이터셋에서 최첨단 성능 달성.

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제안된 방법은 모듈화되어 다른 MOT 알고리즘에 쉽게 적용 가능.

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한계점:

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제안된 방법의 성능 향상이 특정 데이터셋에 국한될 가능성.

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다양한 환경 및 조건에서의 일반화 성능에 대한 추가적인 검증 필요.

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다른 최첨단 MOT 알고리즘과의 비교 분석이 더욱 상세하게 필요할 수 있음.

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