Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation
This work addresses sound localization in videos, which is important for applications in multimedia analysis, but it appears incremental as it builds on existing self-supervised methods with specific enhancements.
The paper tackled the problem of localizing sound sources in videos by developing a self-supervised audio-visual representation learning framework that exploits transformation invariance and equivariance, resulting in significant outperformance on benchmarks like Flickr-SoundNet and VGG-Sound and competitive retrieval performances.
We present a simple yet effective self-supervised framework for audio-visual representation learning, to localize the sound source in videos. To understand what enables to learn useful representations, we systematically investigate the effects of data augmentations, and reveal that (1) composition of data augmentations plays a critical role, i.e. explicitly encouraging the audio-visual representations to be invariant to various transformations~({\em transformation invariance}); (2) enforcing geometric consistency substantially improves the quality of learned representations, i.e. the detected sound source should follow the same transformation applied on input video frames~({\em transformation equivariance}). Extensive experiments demonstrate that our model significantly outperforms previous methods on two sound localization benchmarks, namely, Flickr-SoundNet and VGG-Sound. Additionally, we also evaluate audio retrieval and cross-modal retrieval tasks. In both cases, our self-supervised models demonstrate superior retrieval performances, even competitive with the supervised approach in audio retrieval. This reveals the proposed framework learns strong multi-modal representations that are beneficial to sound localisation and generalization to further applications. \textit{All codes will be available}.