Dual-Stream Decoupled Learning for Temporal Consistency and Speaker Interaction in AVSD
For audio-visual speaker detection, this work breaks a performance plateau by explicitly decoupling conflicting tasks, offering a new architectural paradigm.
Existing AVSD methods struggle to jointly model temporal continuity and social interaction due to conflicting inductive biases. D$^2$Stream decouples these tasks into parallel streams, achieving 95.6% mAP on AVA-ActiveSpeaker and superior generalization on Columbia ASD.
Audio-Visual Speaker Detection (AVSD) hinges on modeling both individual temporal continuity and inter-personal social context. Existing coupled architectures struggle to reconcile these tasks in shared representation spaces due to conflicting inductive biases: temporal modeling favors low-frequency smoothness, while inter-personal interaction requires high-frequency discriminability. We propose D$^2$Stream, a decoupled dual-stream framework that explicitly isolates these functionalities into parallel, task-specific branches. Specifically, the Intra-speaker Temporal Continuity (ITC) stream captures longitudinal stability, whereas the Inter-personal Social Relation (ISR) stream models transversal social cues. Quantitative gradient analysis reveals an evolutionary divergence in update directions, stabilizing at 86.1°, which confirms the inherent task conflict and the effectiveness of our structural decoupling. D$^2$Stream breaks the long-standing performance plateau, achieving a state-of-the-art 95.6% mAP on AVA-ActiveSpeaker and superior generalization on Columbia ASD, all within a lightweight and efficient design.