Learning to localize the sound source in videos without explicit annotations is a novel area of audio-visual research. Existing work in this area focuses on creating attention maps to capture the correlation between the two modalities to localize the source of the sound. In a video, oftentimes, the objects exhibiting movement are the ones generating the sound. In this work, we capture this characteristic by modeling the optical flow in a video as a prior to better aid in localizing the sound source. We further demonstrate that the addition of flow-based attention substantially improves visual sound source localization. Finally, we benchmark our method on standard sound source localization datasets and achieve state-of-the-art performance on the Soundnet Flickr and VGG Sound Source datasets. Code: https://github.com/denfed/heartheflow.
Hear The Flow: Optical Flow-Based Self-Supervised Visual Sound Source Localization
Incorporating optical flow as a prior improves sound source localization in videos without explicit annotations, demonstrating state-of-the-art performance on benchmark datasets.
- Year
- 2022
- Venue
- arXiv 2022
- Authors
- 5
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2211.03019ARXIV-DEFAULT
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