CPM: Class-conditional Prompting Machine for Audio-visual Segmentation
<|reference_start|>CPM: Class-conditional Prompting Machine for Audio-visual Segmentation: 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.<|reference_end|>
@article{chen2024cpm:,
    title={CPM: Class-conditional Prompting Machine for Audio-visual Segmentation},
    author={Yuanhong Chen, Chong Wang, Yuyuan Liu, Hu Wang, Gustavo Carneiro},
    journal={arXiv preprint arXiv:2407.05358},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05358},
    primaryClass={cs.CV}
}