MMA-82 is a multi-domain benchmark with 82 micro-action categories, 77,856 instances from 454 subjects, and protocols for recognition and multi-label detection tasks including cross-domain and few-shot settings.
arXiv preprint arXiv:2603.26586 (2026)
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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.
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.
citing papers explorer
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A New Multi-Domain Benchmark for Micro-Action Recognition and Detection
MMA-82 is a multi-domain benchmark with 82 micro-action categories, 77,856 instances from 454 subjects, and protocols for recognition and multi-label detection tasks including cross-domain and few-shot settings.
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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.