ACVUBench: Audio-Centric Video Understanding Benchmark

• URL: http://arxiv.org/abs/2503.19951v1
• Date: Tue, 25 Mar 2025 16:28:24 GMT
• Title: ACVUBench: Audio-Centric Video Understanding Benchmark
• Authors: Yudong Yang, Jimin Zhuang, Guangzhi Sun, Changli Tang, Yixuan Li, Peihan Li, Yifan Jiang, Wei Li, Zejun Ma, Chao Zhang,
• Abstract summary: ACVUBench is an audio-centric video understanding benchmark.<n>It incorporates 2,662 videos spanning 18 different domains with rich auditory information.<n>It holistically tests the understanding of both audio content and audio-visual interactions in videos.
• Score: 35.77437191750556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio often serves as an auxiliary modality in video understanding tasks of audio-visual large language models (LLMs), merely assisting in the comprehension of visual information. However, a thorough understanding of videos significantly depends on auditory information, as audio offers critical context, emotional cues, and semantic meaning that visual data alone often lacks. This paper proposes an audio-centric video understanding benchmark (ACVUBench) to evaluate the video comprehension capabilities of multimodal LLMs with a particular focus on auditory information. Specifically, ACVUBench incorporates 2,662 videos spanning 18 different domains with rich auditory information, together with over 13k high-quality human annotated or validated question-answer pairs. Moreover, ACVUBench introduces a suite of carefully designed audio-centric tasks, holistically testing the understanding of both audio content and audio-visual interactions in videos. A thorough evaluation across a diverse range of open-source and proprietary multimodal LLMs is performed, followed by the analyses of deficiencies in audio-visual LLMs. Demos are available at https://github.com/lark-png/ACVUBench.

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