most citedSelf-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

29 citations · 43 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20212 cited

SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification

Haocong Rao, Xiping Hu, Jun Cheng +1

Person re-identification via 3D skeletons is an emerging topic with great potential in security-critical applications. Existing methods typically learn body and motion features fro…

cs.CV20212 cited

Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification

Haocong Rao, Shihao Xu, Xiping Hu +2

Skeleton-based person re-identification (Re-ID) is an emerging open topic providing great value for safety-critical applications. Existing methods typically extract hand-crafted fe…

cs.CV202010 cited

Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition

Shihao Xu, Haocong Rao, Xiping Hu +1

In this paper, we focus on unsupervised representation learning for skeleton-based action recognition. Existing approaches usually learn action representations by sequential predic…

cs.CV202029 cited

Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

Haocong Rao, Siqi Wang, Xiping Hu +4

Gait-based person re-identification (Re-ID) is valuable for safety-critical applications, and using only 3D skeleton data to extract discriminative gait features for person Re-ID i…

cs.CV2020

Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition

Haocong Rao, Shihao Xu, Xiping Hu +2

Action recognition via 3D skeleton data is an emerging important topic in these years. Most existing methods either extract hand-crafted descriptors or learn action representations…