Complementarity-Supervised Spectral-Band Routing for Multimodal Emotion Recognition
Multimodal emotion recognition fuses cues such as text, video, and audio to understand individual emotional states. Prior methods face two main limitations: mechanically relying on independent unimodal performance, thereby missing genuine complementary contributions, and coarse-grained fusion conflicting with the fine-grained representations required by emotion tasks. As inconsistent information density across heterogeneous modalities hinders inter-modal feature mining, we propose the Complementarity-Supervised Multi-Band Expert Network, named Atsuko, to model fine-grained complementary features via multi-scale band decomposition and expert collaboration. Specifically, we orthogonally dec...
| Authors | Zhexian Huang, Bo Zhao, Hui Ma et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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