Reproduce “AV-SyncBench: Decoupled Benchmarking of Temporal and Semantic Audio-Visual Synchronization” on OpenResearch
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About this paper
Tianhong Zhou, Mingyang Han, Boyu Li, Yuxuan Jiang, Jiaxin Ye, Dongxiao Wang, Haoxiang Shi, Kunpeng Wang and 3 more
Researchers from Alibaba Group, Tsinghua, and Fudan Universities developed AV-SyncBench, a benchmark designed to independently evaluate the temporal and semantic synchronization abilities of audio-visual feature extractors. Its application showed that current state-of-the-art models typically excel at one dimension of synchronization while demonstrating limitations in the other.
Key insights
- Existing audio-visual models exhibit a "dimensional bias," often optimizing for either temporal synchronization or semantic association, but rarely both simultaneously.
- Performance disparities are evident, with models strong in semantic discrimination often weaker in fine-grained temporal alignment, and vice-versa.
- The benchmark provides a diagnostic tool to understand specific strengths and weaknesses of feature extractors, guiding development toward more balanced representations.
Summary by alphaXiv.
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