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arXiv · 2603.21948

Look, Listen and Segment: Towards Weakly Supervised Audio-visual Semantic Segmentation

Chengzhi Li·Heyan Huang·Ping Jian·Yanghao Zhou
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Abstract

Audio-Visual Semantic Segmentation (AVSS) aligns audio and video at the pixel level but requires costly per-frame annotations. We introduce Weakly Supervised Audio-Visual Semantic Segmentation (WSAVSS), which uses only video-level labels to generate per-frame semantic masks of sounding objects. We decompose WSAVSS into looking, listening, and segmentation, and propose Progressive Cross-modal Alignment for Semantics (PCAS) with two modules: *Looking-before-Listening* and *Listening-before-Segmentation*. PCAS builds a classification task to train the audio-visual encoder using video labels, injects visual semantic prompts to enhance frame-level audio understanding, and then applies progressive contrastive alignment to map audio categories to image regions without mask annotations. Experiments show PCAS achieves state-of-the-art performance among weakly supervised methods on AVS and remains competitive with fully supervised baselines on AVSS, validating its effectiveness.

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Chengzhi Li, Heyan Huang, Ping Jian, Yanghao Zhou. 2026-03-23. Look, Listen and Segment: Towards Weakly Supervised Audio-visual Semantic Segmentation. https://arxiv.org/abs/2603.21948

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