A decoupled adapter with independent spatial-temporal branches via depthwise convolutions and a dynamic augmentation strategy for long-tail data achieves first place with F1 0.43808 in a micro-gesture recognition challenge.
arXiv preprint arXiv:2507.09512 (2025)
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A multi-modal system combining skeleton/heatmap/RGB features with cross-modal pseudo-labeling and semantic losses achieves 68.13% F1-score and 4th place on the MiGA-IJCAI micro-gesture challenge.
DyFADet+ extends a prior detector with gated RGB-skeleton fusion and reports 40.88 F1 on the SMG dataset for micro-gesture online recognition.
Ensemble of self-supervised RGB model and supervised models achieves new SOTA of 74.419% on iMiGUE micro-gesture dataset.
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.
citing papers explorer
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Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition
A decoupled adapter with independent spatial-temporal branches via depthwise convolutions and a dynamic augmentation strategy for long-tail data achieves first place with F1 0.43808 in a micro-gesture recognition challenge.
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A Multi-Modal Framework with Cross-Subject Pseudo-Labeling and Semantic Alignment for Micro-Gesture Recognition
A multi-modal system combining skeleton/heatmap/RGB features with cross-modal pseudo-labeling and semantic losses achieves 68.13% F1-score and 4th place on the MiGA-IJCAI micro-gesture challenge.
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Motion Reinforces Appearance: RGB-Skeleton Gated Residual Fusion for Micro-Gesture Online Recognition
DyFADet+ extends a prior detector with gated RGB-skeleton fusion and reports 40.88 F1 on the SMG dataset for micro-gesture online recognition.
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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.