547 citations · 612 across the 6 of their papers we have counts for
6 papers
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition
Lei Shi, Yifan Zhang, Jian Cheng +1
Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered. Recently, there…
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition
Lei Shi, Yifan Zhang, Jian Cheng +1
Dynamic skeletal data, represented as the 2D/3D coordinates of human joints, has been widely studied for human action recognition due to its high-level semantic information and env…
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition
Lei Shi, Yifan Zhang, Jian Cheng +1
Skeleton data, which consists of only the 2D/3D coordinates of the human joints, has been widely studied for human action recognition. Existing methods take the semantics as prior…
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
Lei Shi, Yifan Zhang, Jian Cheng +1
Graph convolutional networks (GCNs), which generalize CNNs to more generic non-Euclidean structures, have achieved remarkable performance for skeleton-based action recognition. How…
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision
Lei Shi, Yifan Zhang, Jian Cheng +1
Pose-based action recognition has drawn considerable attention recently. Existing methods exploit the joint positions to extract the body-part features from the activation map of t…
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
Lei Shi, Yifan Zhang, Jian Cheng +1
In skeleton-based action recognition, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have achieved remarkable performance. Howe…