Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning

Open-sci-ref-0.01: open and reproducible reference baselines for language model and dataset comparison

Similarity-based Outlier Detection for Noisy Object Re-Identification Using Beta Mixtures

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining

Musculoskeletal simulation of limb movement biomechanics in Drosophila melanogaster

Between a Rock and a Hard Place: Exploiting Ethical Reasoning to Jailbreak LLMs

Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees

The Architecture of AI Transformation: Four Strategic Patterns and an Emerging Frontier

IS${}^3$ : Generic Impulsive--Stationary Sound Separation in Acoustic Scenes using Deep Filtering

Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Space

HiddenObject: Modality-Agnostic Fusion for Multimodal Hidden Object Detection

Input-Time Scaling

Counterfactual Probabilistic Diffusion with Expert Models

Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments

Bridging the Gap: A Framework for Real-World Video Deepfake Detection via Social Network Compression Emulation

Imposing AI: Deceptive design patterns against sustainability

LMAR: Language Model Augmented Retriever for Domain-specific Knowledge Indexing

Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems

Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges

Atherosclerosis through Hierarchical Explainable Neural Network Analysis

Survivability of Backdoor Attacks on Unconstrained Face Recognition Systems

Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding

HiLight: A Hierarchical Reinforcement Learning Framework with Global Adversarial Guidance for Large-Scale Traffic Signal Control

AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning

OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrieval

The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance?

A Framework for Testing and Adapting REST APIs as LLM Tools

D\'ej\`a Vu: Multilingual LLM Evaluation through the Lens of Machine Translation Evaluation

JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse

Prompt Programming: A Platform for Dialogue-based Computational Problem Solving with Generative AI Models

Auxiliary Discrminator Sequence Generative Adversarial Networks (ADSeqGAN) for Few Sample Molecule Generation

Neural Force Field: Few-shot Learning of Generalized Physical Reasoning

Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework

A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection

Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning

Polish-English medical knowledge transfer: A new benchmark and results

InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction

AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models

A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research

A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts

The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design

Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need

Slaves to the Law of Large Numbers: An Asymptotic Equipartition Property for Perplexity in Generative Language Models

Interpretable Data-driven Anomaly Detection in Industrial Processes with ExIFFI

Analyzing the Impact of Adversarial Examples on Explainable Machine Learning

TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential

Sufficient Invariant Learning for Distribution Shift

TORSO: Template-Oriented Reasoning Towards General Tasks

ForTIFAI: Fending Off Recursive Training Induced Failure for AI Models

Oyster-I: Beyond Refusal -- Constructive Safety Alignment for Responsible Language Models

Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark

QuantX: A Framework for Hardware-Aware Quantization of Generative AI Workloads

Learning to Plan with Personalized Preferences

Multi-Turn Human-LLM Interaction Through the Lens of a Two-Way Intelligibility Protocol

Spatio-Temporal Graphical Counterfactuals: An Overview

Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective

Is In-Context Learning Learning?

Multimodal SAM-adapter for Semantic Segmentation

Diversified recommendations of cultural activities with personalized determinantal point processes

Improving Audio Event Recognition with Consistency Regularization

Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms

Towards Understanding Visual Grounding in Visual Language Models

GLAM: Geometry-Guided Local Alignment for Multi-View VLP in Mammography

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Data

We Need a New Ethics for a World of AI Agents

SignClip: Leveraging Mouthing Cues for Sign Language Translation by Multimodal Contrastive Fusion

Openness in AI and downstream governance: A global value chain approach

SI-FACT: Mitigating Knowledge Conflict via Self-Improving Faithfulness-Aware Contrastive Tuning

Benchmark of stylistic variation in LLM-generated texts

BenchECG and xECG: a benchmark and baseline for ECG foundation models

Efficient Learning-Based Control of a Legged Robot in Lunar Gravity

Population-Aligned Persona Generation for LLM-based Social Simulation

Realism Control One-step Diffusion for Real-World Image Super-Resolution

Generating Energy-Efficient Code via Large-Language Models -- Where are we now?

Established Psychometric vs. Ecologically Valid Questionnaires: Rethinking Psychological Assessments in Large Language Models

Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks

TwinTac: A Wide-Range, Highly Sensitive Tactile Sensor with Real-to-Sim Digital Twin Sensor Model

Multimodal Mathematical Reasoning Embedded in Aerial Vehicle Imagery: Benchmarking, Analysis, and Exploration

Reinforcement learning for spin torque oscillator tasks

Exploring Expert Specialization through Unsupervised Training in Sparse Mixture of Experts

Intrinsic Dimension Estimating Autoencoder (IDEA) Using CancelOut Layer and a Projected Loss

Unsupervised Hallucination Detection by Inspecting Reasoning Processes

Drone-Based Multispectral Imaging and Deep Learning for Timely Detection of Branched Broomrape in Tomato Farms

Securing LLM-Generated Embedded Firmware through AI Agent-Driven Validation and Patching

Large Language Models Meet Legal Artificial Intelligence: A Survey

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

Zero-Shot Referring Expression Comprehension via Visual-Language True/False Verification

Adaptive Token Merging for Efficient Transformer Semantic Communication at the Edge

SmartCoder-R1: Towards Secure and Explainable Smart Contract Generation with Security-Aware Group Relative Policy Optimization

WALL: A Web Application for Automated Quality Assurance using Large Language Models

An Autoencoder and Vision Transformer-based Interpretability Analysis of the Differences in Automated Staging of Second and Third Molars

Tackling One Health Risks: How Large Language Models are leveraged for Risk Negotiation and Consensus-building

Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining

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Emulating Public Opinion: A Proof-of-Concept of AI-Generated Synthetic Survey Responses for the Chilean Case

Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks

Multimodal SAM-adapter for Semantic Segmentation

Created by

Haebom

저자

Iacopo Curti, Pierluigi Zama Ramirez, Alioscia Petrelli, Luigi Di Stefano

개요

본 논문은 다양한 응용 분야에서 중요한 역할을 하는 의미론적 분할에 대해 다룹니다. 기존의 심층 학습 기반 의미론적 분할 방법들은 조명 불량, 물체 가림, 악천후와 같은 어려운 조건에 취약합니다. 이러한 한계를 극복하기 위해, 본 논문에서는 다중 모달 방법을 이용하여 SAM(Segment Anything Model)을 확장한 MM SAM-adapter 프레임워크를 제시합니다. MM SAM-adapter는 추가적인 센서 데이터(예: LiDAR, 적외선)를 SAM의 RGB 특징에 융합하는 어댑터 네트워크를 사용하여, RGB 특징의 우수한 일반화 능력을 유지하면서 보조 모달리티가 추가적인 정보를 제공할 때만 선택적으로 통합합니다. DeLiVER, FMB, MUSES 세 가지 벤치마크에서 최첨단 성능을 달성하며, RGB-easy 및 RGB-hard 하위 집합으로 나누어 분석한 결과, 유리한 조건과 열악한 조건 모두에서 경쟁 방법보다 우수한 성능을 보여줍니다. 코드는 https://github.com/iacopo97/Multimodal-SAM-Adapter 에서 이용 가능합니다.

GitHub - iacopo97/Multimodal-SAM-Adapter: This repository contains download links to dataset, code snippets, and trained deep models of our work "Multimodal SAM-Adapter for Semantic Segmentation", by Iacopo Curti*, Pierluigi Zama Ramirez*, Alioscia Petrelli* , and Luigi Di Stefano*. * Equal Contribution University of Bologna

This repository contains download links to dataset, code snippets, and trained deep models of our work "Multimodal SAM-Adapter for Semantic Segmentation", by Iacopo Curti*, Pierl...

시사점, 한계점

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시사점:

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SAM의 강점을 유지하면서 다중 모달 정보를 효과적으로 활용하는 새로운 프레임워크를 제시.

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다양한 어려운 조건에서도 강건한 의미론적 분할 성능을 달성.

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세 가지 벤치마크 데이터셋에서 최첨단 성능을 기록.

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모달리티 기여도에 대한 분석을 통해 방법의 효과성을 입증.

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공개된 코드를 통해 재현성 및 확장성 확보.

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한계점:

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특정 벤치마크 데이터셋에 대한 성능 평가 결과만 제시되어 일반화 가능성에 대한 추가 검증 필요.

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어댑터 네트워크의 설계 및 매개변수 최적화 과정에 대한 자세한 설명 부족.

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다양한 모달리티 조합에 대한 추가적인 실험 및 분석 필요.

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