Ensemble of self-supervised RGB model and supervised models achieves new SOTA of 74.419% on iMiGUE micro-gesture dataset.
arXiv preprint arXiv:2602.08057 (2026)
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A competition-winning multi-modal model for hidden emotion recognition integrates static and dynamic pose features via cross-attention and MIL pooling while noting representation collapse in vision foundation models on micro-dynamic tasks.
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Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition
Ensemble of self-supervised RGB model and supervised models achieves new SOTA of 74.419% on iMiGUE micro-gesture dataset.
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Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition
A competition-winning multi-modal model for hidden emotion recognition integrates static and dynamic pose features via cross-attention and MIL pooling while noting representation collapse in vision foundation models on micro-dynamic tasks.