Weakly-Supervised Audio-Visual Segmentation

• URL: http://arxiv.org/abs/2311.15080v1
• Date: Sat, 25 Nov 2023 17:18:35 GMT
• Title: Weakly-Supervised Audio-Visual Segmentation
• Authors: Shentong Mo, Bhiksha Raj
• Abstract summary: We present a novel Weakly-Supervised Audio-Visual framework, namely WS-AVS, that can learn multi-scale audio-visual alignment with multi-instance contrastive learning for audio-visual segmentation. Experiments on AVSBench demonstrate the effectiveness of our WS-AVS in the weakly-supervised audio-visual segmentation of single-source and multi-source scenarios.
• Score: 44.632423828359315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-visual segmentation is a challenging task that aims to predict pixel-level masks for sound sources in a video. Previous work applied a comprehensive manually designed architecture with countless pixel-wise accurate masks as supervision. However, these pixel-level masks are expensive and not available in all cases. In this work, we aim to simplify the supervision as the instance-level annotation, i.e., weakly-supervised audio-visual segmentation. We present a novel Weakly-Supervised Audio-Visual Segmentation framework, namely WS-AVS, that can learn multi-scale audio-visual alignment with multi-scale multiple-instance contrastive learning for audio-visual segmentation. Extensive experiments on AVSBench demonstrate the effectiveness of our WS-AVS in the weakly-supervised audio-visual segmentation of single-source and multi-source scenarios.

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