XGC-AVis: Towards Audio-Visual Content Understanding with a Multi-Agent Collaborative System
Abstract
In this paper, we propose XGC-AVis, a multi-agent framework that enhances the audio-video temporal alignment capabilities of multimodal large models (MLLMs) and improves the efficiency of retrieving key video segments through 4 stages: perception, planning, execution, and reflection. We further introduce XGC-AVQuiz, the first benchmark aimed at comprehensively assessing MLLMs' understanding capabilities in both real-world and AI-generated scenarios. XGC-AVQuiz consists of 2,685 question-answer pairs across 20 tasks, with two key innovations: 1) AIGC Scenario Expansion: The benchmark includes 2,232 videos, comprising 1,102 professionally generated content (PGC), 753 user-generated content (UGC), and 377 AI-generated content (AIGC). These videos cover 10 major domains and 53 fine-grained categories. 2) Quality Perception Dimension: Beyond conventional tasks such as recognition, localization, and reasoning, we introduce a novel quality perception dimension. This requires MLLMs to integrate low-level sensory capabilities with high-level semantic understanding to assess audio-visual quality, synchronization, and coherence. Experimental results on XGC-AVQuiz demonstrate that current MLLMs struggle with quality perception and temporal alignment tasks. XGC-AVis improves these capabilities without requiring additional training, as validated on two benchmarks.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the development of XGC-AVis, a multi-agent framework that enhances MLLMs' audio-visual understanding capabilities while introducing a novel benchmark, XGC-AVQuiz, to evaluate these advancements comprehensively. The work addresses critical gaps in multimodal learning, providing a structured approach to improving temporal alignment and quality perception in audio-visual tasks, which is essential for the future of AI applications in multimedia contexts.
Methodology
The paper introduces XGC-AVis, a multi-agent system designed to enhance the audio-visual understanding of multimodal large models (MLLMs) through a structured four-stage process: perception, planning, execution, and reflection. This methodology is innovative as it integrates a novel quality perception dimension, which assesses the synchronization and coherence of audio-visual content beyond traditional recognition tasks. The introduction of the XGC-AVQuiz benchmark, which includes diverse video sources and a multi-level task hierarchy, is a significant contribution that addresses existing limitations in multimodal benchmarks.
Results & evaluation
The experimental section is robust, featuring extensive evaluations across various MLLMs, including both open-source and proprietary models. The results demonstrate that XGC-AVis significantly improves performance in quality perception and temporal alignment tasks, highlighting the effectiveness of the proposed methodology. The use of a diverse dataset comprising professionally generated, user-generated, and AI-generated content enhances the generalizability of the findings.
Reproducibility
The authors emphasize reproducibility by detailing the framework and experimental setup, including the use of open-source models and publicly available APIs. They also plan to make their code and dataset publicly available, which is a positive aspect for future research.
Limitations
One limitation noted is the reliance on existing MLLMs, which may have inherent biases or limitations that affect the performance of XGC-AVis. Additionally, while the benchmark is comprehensive, the focus on specific tasks may not cover all aspects of audio-visual understanding, potentially limiting its applicability.
Broader impact
The proposed system has significant implications for various applications, including video content analysis, automated content moderation, and enhancing user experiences in multimedia applications. By improving MLLMs' understanding of audio-visual content, this research can contribute to advancements in AI-driven systems that require nuanced comprehension of multimodal information. The main contribution of this paper is the development of XGC-AVis, a multi-agent framework that enhances MLLMs' audio-visual understanding capabilities while introducing a novel benchmark, XGC-AVQuiz, to evaluate these advancements comprehensively. The work addresses critical gaps in multimodal learning, providing a structured approach to improving temporal alignment and quality perception in audio-visual tasks, which is essential for the future of AI applications in multimedia contexts.
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