CPM: Class-conditional Prompting Machine for Audio-visual Segmentation

• URL: http://arxiv.org/abs/2407.05358v3
• Date: Sun, 29 Sep 2024 05:48:33 GMT
• Title: CPM: Class-conditional Prompting Machine for Audio-visual Segmentation
• Authors: Yuanhong Chen, Chong Wang, Yuyuan Liu, Hu Wang, Gustavo Carneiro,
• Abstract summary: Class-conditional Prompting Machine (CPM) improves bipartite matching with a learning strategy combining class-agnostic queries with class-conditional queries. We conduct experiments on AVS benchmarks, demonstrating that our method achieves state-of-the-art (SOTA) segmentation accuracy.
• Score: 17.477225065057993
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Audio-visual segmentation (AVS) is an emerging task that aims to accurately segment sounding objects based on audio-visual cues. The success of AVS learning systems depends on the effectiveness of cross-modal interaction. Such a requirement can be naturally fulfilled by leveraging transformer-based segmentation architecture due to its inherent ability to capture long-range dependencies and flexibility in handling different modalities. However, the inherent training issues of transformer-based methods, such as the low efficacy of cross-attention and unstable bipartite matching, can be amplified in AVS, particularly when the learned audio query does not provide a clear semantic clue. In this paper, we address these two issues with the new Class-conditional Prompting Machine (CPM). CPM improves the bipartite matching with a learning strategy combining class-agnostic queries with class-conditional queries. The efficacy of cross-modal attention is upgraded with new learning objectives for the audio, visual and joint modalities. We conduct experiments on AVS benchmarks, demonstrating that our method achieves state-of-the-art (SOTA) segmentation accuracy.

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