Ordering Matters: Rank-Aware Selective Fusion for Blended Emotion Recognition
Blended emotion recognition is challenging because emotions are often expressed as mixtures of subtle and overlapping multimodal cues rather than a single dominant signal. We propose a rank-aware multi-encoder framework that selectively combines complementary representations from diverse pre-extracted video and audio encoders. Our method projects heterogeneous encoder features into a shared latent space, estimates sample-wise encoder importance through an attention-based gating module, and fuses only the top-n most informative encoders. To better model blended emotions, we decouple prediction into presence and salience heads and align them through probability-level fusion. We further inco...
| Authors | Junghyun Lee, Hyunseo Kim, Hanna Jang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 May 2026 |
| Last updated at source | 24 May 2026 |
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