Selective Noise Suppression and Discriminative Mutual Interaction for Robust Audio-Visual Segmentation
This work addresses robust audio-visual segmentation for applications in multi-source and complex scenes, representing an incremental improvement over existing methods.
The paper tackled the problem of segmenting sounding objects in dynamic visual scenes by proposing SDAVS, which uses a Selective Noise-Resilient Processor and Discriminative Audio-Visual Mutual Fusion to suppress audio noise and enhance interactions, achieving state-of-the-art performance on benchmark datasets.
The ability to capture and segment sounding objects in dynamic visual scenes is crucial for the development of Audio-Visual Segmentation (AVS) tasks. While significant progress has been made in this area, the interaction between audio and visual modalities still requires further exploration. In this work, we aim to answer the following questions: How can a model effectively suppress audio noise while enhancing relevant audio information? How can we achieve discriminative interaction between the audio and visual modalities? To this end, we propose SDAVS, equipped with the Selective Noise-Resilient Processor (SNRP) module and the Discriminative Audio-Visual Mutual Fusion (DAMF) strategy. The proposed SNRP mitigates audio noise interference by selectively emphasizing relevant auditory cues, while DAMF ensures more consistent audio-visual representations. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on benchmark AVS datasets, especially in multi-source and complex scenes. \textit{The code and model are available at https://github.com/happylife-pk/SDAVS}.