Sound Source Localization is All about Cross-Modal Alignment
September 19, 2023 Β· Declared Dead Β· π IEEE International Conference on Computer Vision
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Authors
Arda Senocak, Hyeonggon Ryu, Junsik Kim, Tae-Hyun Oh, Hanspeter Pfister, Joon Son Chung
arXiv ID
2309.10724
Category
cs.CV: Computer Vision
Cross-listed
cs.AI,
cs.MM,
cs.SD,
eess.AS
Citations
31
Venue
IEEE International Conference on Computer Vision
Last Checked
6 months ago
Abstract
Humans can easily perceive the direction of sound sources in a visual scene, termed sound source localization. Recent studies on learning-based sound source localization have mainly explored the problem from a localization perspective. However, prior arts and existing benchmarks do not account for a more important aspect of the problem, cross-modal semantic understanding, which is essential for genuine sound source localization. Cross-modal semantic understanding is important in understanding semantically mismatched audio-visual events, e.g., silent objects, or off-screen sounds. To account for this, we propose a cross-modal alignment task as a joint task with sound source localization to better learn the interaction between audio and visual modalities. Thereby, we achieve high localization performance with strong cross-modal semantic understanding. Our method outperforms the state-of-the-art approaches in both sound source localization and cross-modal retrieval. Our work suggests that jointly tackling both tasks is necessary to conquer genuine sound source localization.
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