AV-Odyssey 實驗平台:您的多模態語言模型真的能理解視聽資訊嗎?
AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?
摘要
Summary
AI-Generated Summary
Paper Overview
This literature evaluates Multimodal Large Language Models (MLLMs) on audio-visual tasks using the AV-Odyssey Bench. It identifies limitations in current models' audio-visual comprehension and integration, emphasizing the need for advancements in this area.
Core Contribution
The study introduces the AV-Odyssey Bench, a comprehensive audio-visual benchmark challenging MLLMs with 4,555 problems across 26 tasks, highlighting deficiencies in audio-visual integration.
Research Context
The research addresses the gap in understanding MLLMs' audio-visual capabilities, focusing on tasks like pitch comparison, sound duration recognition, and audio-visual information integration.
Keywords
Multimodal Large Language Models (MLLMs), AV-Odyssey Bench, Audio-Visual Integration, Benchmark Evaluation, Audio Understanding, Vision Understanding
Background
This study aims to evaluate MLLMs' performance on audio-visual tasks, highlighting challenges in discriminating sound volume, pitch, and integrating audio-visual information effectively.
Research Gap
Existing literature lacks in-depth evaluation of MLLMs' audio-visual comprehension, especially in discriminating sound attributes and processing complex audio-visual tasks.
Technical Challenges
MLLMs face difficulties in discriminating sound volume, higher pitch, and integrating audio and visual modalities effectively for accurate inference.
Prior Approaches
Previous benchmarks did not adequately test MLLMs' audio-visual integration capabilities, lacking complexity and domain focus compared to the AV-Odyssey Bench.
Methodology
The study employs the AV-Odyssey Bench to evaluate MLLMs across various audio-visual tasks, presenting detailed data distribution, evaluation results, and model performance metrics.
Theoretical Foundation
The AV-Odyssey Bench comprises 26 tasks challenging MLLMs in audio-visual integration, requiring models to process audio and visual cues effectively for accurate responses.
Technical Architecture
Models like Unified-IO, VideoLLaMA, Gemini, and GPT-4o are tested on the AV-Odyssey Bench, highlighting strengths and weaknesses in audio-visual comprehension.
Implementation Details
Data curation involves unbiased collection of audio and visual data, while quality control filters ensure fair evaluation. Models are tested in a zero-shot setting without finetuning.
Innovation Points
The AV-Odyssey Bench introduces a novel evaluation method for MLLMs, emphasizing audio-visual integration and providing insights into model limitations.
Experimental Validation
The study experimentally validates MLLMs' performance on the AV-Odyssey Bench, showcasing challenges in audio-visual integration and identifying errors in audio understanding and reasoning.
Setup
Models like Gemini, Reka, Unified-IO, and GPT-4o are tested on the AV-Odyssey Bench, revealing limitations in audio-visual comprehension and integration.
Metrics
Performance metrics like accuracy are reported for tasks involving instrument recognition, singer recognition, gunshot recognition, and other audio-visual challenges.
Results
Models exhibit varying performance on different tasks, with the top-performing model achieving 34.5% accuracy, highlighting the difficulty of the benchmark.
Comparative Analysis
Comparison between open-source and closed-source models reveals similar performance levels, indicating the universal challenge in audio-visual integration for MLLMs.
Impact and Implications
The study's findings underscore the limitations in current MLLMs' audio-visual understanding, emphasizing the need for improved audio-visual integration and model development.
Key Findings
The AV-Odyssey Bench exposes deficiencies in MLLMs' audio-visual comprehension, indicating the necessity for advancements in true audio-visual integration.
Limitations
Current MLLMs struggle with audio understanding errors and reasoning challenges, hindering accurate audio-visual inference and integration.
Future Directions
Future research should focus on enhancing multi-modal reasoning, improving audio-visual integration, and developing datasets that challenge MLLMs' audio-visual capabilities.
Practical Significance
Advancements in audio-visual integration can lead to more human-like audio-visual understanding in MLLMs, benefiting various applications requiring multi-modal comprehension.