TACO: Training-free Sound Prompted Segmentation via Semantically Constrained Audio-visual CO-factorization

Abstract:Large-scale pre-trained audio and image models demonstrate an unprecedented degree of generalization, making them suitable for a wide range of applications. Here, we tackle the specific task of sound-prompted segmentation, aiming to segment image regions corresponding to objects heard in an audio signal. Most existing approaches tackle this problem by fine-tuning pre-trained models or by training additional modules specifically for the task. We adopt a different strategy: we introduce a training-free approach that leverages Non-negative Matrix Factorization (NMF) to co-factorize audio and visual features from pre-trained models so as to reveal shared interpretable concepts. These concepts are passed on to an open-vocabulary segmentation model for precise segmentation maps. By using frozen pre-trained models, our method achieves high generalization and establishes state-of-the-art performance in unsupervised sound-prompted segmentation, significantly surpassing previous unsupervised methods.

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Image and Video Processing (eess.IV)

arXiv:2412.01488 [eess.AS]

(or arXiv:2412.01488v4 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2412.01488 Focus to learn more arXiv-issued DOI via DataCite

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