17 citations · 33 across the 6 of their papers we have counts for
6 papers
Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
Mengyuan Liu, Hong Liu, Tianyu Guo
Considering the instance-level discriminative ability, contrastive learning methods, including MoCo and SimCLR, have been adapted from the original image representation learning ta…
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation
Jingwen Guo, Hong Liu, Shitong Sun +3
Existing skeleton-based action recognition methods typically follow a centralized learning paradigm, which can pose privacy concerns when exposing human-related videos. Federated L…
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action Recognition
Zhan Chen, Hong Liu, Tianyu Guo +3
Self-supervised skeleton-based action recognition with contrastive learning has attracted much attention. Recent literature shows that data augmentation and large sets of contrasti…
GraphMLP: A Graph MLP-Like Architecture for 3D Human Pose Estimation
Wenhao Li, Mengyuan Liu, Hong Liu +4
Modern multi-layer perceptron (MLP) models have shown competitive results in learning visual representations without self-attention. However, existing MLP models are not good at ca…
Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer
Tao Wang, Hong Liu, Pinhao Song +2
Occluded person re-identification is a challenging task as human body parts could be occluded by some obstacles (e.g. trees, cars, and pedestrians) in certain scenes. Some existing…
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition
Tianyu Guo, Hong Liu, Zhan Chen +3
In recent years, self-supervised representation learning for skeleton-based action recognition has been developed with the advance of contrastive learning methods. The existing con…