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.
Crossl: Cross-modal self-supervised learning for time- series through latent masking,
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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.