T-VSL: Text-Guided Visual Sound Source Localization in Mixtures
<|reference_start|>T-VSL: Text-Guided Visual Sound Source Localization in Mixtures: Visual sound source localization poses a significant challenge in identifying the semantic region of each sounding source within a video. Existing self-supervised and weakly supervised source localization methods struggle to accurately distinguish the semantic regions of each sounding object, particularly in multi-source mixtures. These methods often rely on audio-visual correspondence as guidance, which can lead to substantial performance drops in complex multi-source localization scenarios. The lack of access to individual source sounds in multi-source mixtures during training exacerbates the difficulty of learning effective audio-visual correspondence for localization. To address this limitation, in this paper, we propose incorporating the text modality as an intermediate feature guide using tri-modal joint embedding models (e.g., AudioCLIP) to disentangle the semantic audio-visual source correspondence in multi-source mixtures. Our framework, dubbed T-VSL, begins by predicting the class of sounding entities in mixtures. Subsequently, the textual representation of each sounding source is employed as guidance to disentangle fine-grained audio-visual source correspondence from multi-source mixtures, leveraging the tri-modal AudioCLIP embedding. This approach enables our framework to handle a flexible number of sources and exhibits promising zero-shot transferability to unseen classes during test time. Extensive experiments conducted on the MUSIC, VGGSound, and VGGSound-Instruments datasets demonstrate significant performance improvements over state-of-the-art methods. Code is released at https://github.com/enyac-group/T-VSL/tree/main<|reference_end|>
@article{mahmud2024t-vsl:,
    title={T-VSL: Text-Guided Visual Sound Source Localization in Mixtures},
    author={Tanvir Mahmud, Yapeng Tian, Diana Marculescu},
    journal={IEEE/CVF Computer Vision and Pattern Recognition (CVPR)
  Conference, 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01751},
    primaryClass={cs.CV cs.SD eess.AS}
}