$C^3$ASD: Multi-Level Consistency-Driven Representation Learning for Robust Active Speaker Detection
Active Speaker Detection determines whether a visible person in a video is speaking at each moment. While recent audio-visual fusion methods perform well on clean data, they degrade under real-world corruptions such as background noise, occlusion, or simultaneous modality degradation. We attribute this limitation to the absence of explicit consistency constraints that promote robust, semantically aligned representations across modalities. Without such guidance, models tend to learn fragile modality-specific shortcuts that fail under corrupted conditions. We propose $C^3$ASD, a multi-level consistency-driven framework with three complementary constraints: embedding-level inter-modality con...
| Authors | Jin Hong, Jisoo Park, Junseok Kwon |
|---|---|
| Primary category | cs.CV |
| Published | 3 July 2026 |
| Last updated at source | 26 September 2026 |
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