Search Results for author:
Found 6 papers, 2 papers with code
Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning
Moreover, combining these two paradigms in a naive manner leaves the synergy between them untapped and can lead to interference in training.
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition
However, these methods treat the motion and static parts equally, and lack an adaptive design for different parts, which has a negative impact on the accuracy of action recognition.
Ranked #82 on
Skeleton Based Action Recognition
on NTU RGB+D
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations
In this paper, we investigate the potential of adopting strong augmentations and propose a general hierarchical consistent contrastive learning framework (HiCLR) for skeleton-based action recognition.
S$^{5}$Mars: Semi-Supervised Learning for Mars Semantic Segmentation
We first present a newdataset S5Mars for Semi-SuperviSed learning on Mars Semantic Segmentation, which contains 6K high-resolution images and is sparsely annotated based on confidence, ensuring the high quality of labels.
Semi-Supervised Learning for Mars Imagery Classification and Segmentation
For segmentation, we extend supervised inter-class contrastive learning into an element-wise mode and use online pseudo labels for supervision on unlabeled areas.
MS$^2$L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
To realize this goal, we integrate motion prediction, jigsaw puzzle recognition, and contrastive learning to learn skeleton features from different aspects.
