BROWSE
Detail
Journal Article
Hearing and Seeing Through CLIP: A Framework for Self-Supervised Sound Source Localization
- Authors
- Sooyoung Park, Arda Senocak, Joon Son Chung
- Issue Date
- 2026-03
- Citation
- International Journal of Computer Vision, v.134, pp.1-28
- ISSN
- 0920-5691
- Publisher
- Springer Nature
- Language
- English
- Type
- Journal Article
- Abstract
- Large-scale vision-language models demonstrate strong multimodal alignment and generalization across diverse tasks. Among them, CLIP stands out as one of the most successful approaches. In this work, we extend the application of CLIP to sound source localization, proposing a self-supervised method operates without explicit text input. We introduce a framework that maps audios into tokens compatible with CLIP’s text encoder, producing audio-driven embeddings. These embeddings are used to generate sounding region masks, from which visual features are extracted and aligned with the audio embeddings through a contrastive audio-visual correspondence objective. Our findings show that alignment knowledge of pre-trained multimodal foundation model enables our method to generate more complete and compact localization for sounding objects. We further propose an LLM-guided extension that distills object-aware audio-visual scene understanding into the model during training to enhance alignment. Extensive experiments across five diverse tasks demonstrate that our method, in all variants, outperforms state-of-the-art approaches and achieves strong generalization in zero-shot settings.
- Keyword
- Audio-visual Learning, Sound Source Localization, Discriminative Learning
- KSP Keywords
- Audio-visual, Discriminative learning, Multimodal alignment, Object-aware, Scene Understanding, Text input, Visual Features, Visual learning, Zero-shot, language models, large-scale
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
