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arXiv · 2605.21417

Ordering Matters: Rank-Aware Selective Fusion for Blended Emotion Recognition

Junghyun Lee·Hyunseo Kim·Hanna Jang·Junhyug Noh
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Abstract

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 incorporate feature-level unsupervised domain adaptation without pseudo-labeling to improve robustness under distribution shift. Experiments on the BlEmoRE challenge show that the proposed framework outperforms strong individual encoders and naïve multi-encoder fusion baselines. Our final system ranked 2nd in the competition, supporting the effectiveness of rank-aware selective fusion for fine-grained blended emotion recognition.

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Junghyun Lee, Hyunseo Kim, Hanna Jang, Junhyug Noh. 2026-05-24. Ordering Matters: Rank-Aware Selective Fusion for Blended Emotion Recognition. https://arxiv.org/abs/2605.21417

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