CLMM is a two-stage contrastive learning framework using CNN-DiffTransformer encoders and dual-branch fusion to improve multimodal human activity recognition under limited labels.
Utd-mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor,
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Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data
CLMM is a two-stage contrastive learning framework using CNN-DiffTransformer encoders and dual-branch fusion to improve multimodal human activity recognition under limited labels.