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Found 3 papers, 1 papers with code
Attack is Good Augmentation: Towards Skeleton-Contrastive Representation Learning
In particular, we propose a novel Attack-Augmentation Mixing-Contrastive learning (A$^2$MC) to contrast hard positive features and hard negative features for learning more robust skeleton representations.
Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action Recognition
Most semi-supervised skeleton-based action recognition approaches aim to learn the skeleton action representations only at the joint level, but neglect the crucial motion characteristics at the coarser-grained body (e. g., limb, trunk) level that provide rich additional semantic information, though the number of labeled data is limited.
Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition
Moreover, we present a new Spatial-squeezing Temporal-contrasting Loss (STL), a new Temporal-squeezing Spatial-contrasting Loss (TSL), and the Global-contrasting Loss (GL) to contrast the spatial-squeezing joint and motion features at the frame level, temporal-squeezing joint and motion features at the joint level, as well as global joint and motion features at the skeleton level.
