• Corpus ID: 209500977

Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition

  title={Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition},
  author={Jialin Gao and Tong He and Xiaoping Zhou and Shiming Ge},
A collection of approaches based on graph convolutional networks have proven success in skeleton-based action recognition by exploring neighborhood information and dense dependencies between intra-frame joints. However, these approaches usually ignore the spatial-temporal global context as well as the local relation between inter-frame and intra-frame. In this paper, we propose a focusing and diffusion mechanism to enhance graph convolutional networks by paying attention to the kinematic… 

Figures and Tables from this paper

Temporal Extension Module for Skeleton-Based Action Recognition
This work presents a module that extends the temporal graph of a graph convolutional network (GCN) for action recognition with a sequence of skeletons and is a simple yet effective method to extract correlated features of multiple joints in human movement.
Selective Hypergraph Convolutional Networks for Skeleton-based Action Recognition
A novel Selective Hypergraph Convolution Network is proposed, dubbed Selective-HCN, which stacks two key modules: selective-scale Hypergraphconvolution (SHC) and Selective -frame Temporal Convolution (STC), which represents the human skeleton as the graph and hypergraph to fully extract multi-scale information, and selectively fuse features at various scales.
Multi-stream mixed graph convolutional networks for skeleton-based action recognition
The final model, the multi-stream MGCN, is validated on two challenging datasets, achieving the state-of-the-art performance on the NTU-RGBD dataset and making competitive performance on a kinetics-skeleton dataset.
Whole and Part Adaptive Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition
A whole and part adaptive fusion graph convolution neural network (WPGCN) that outperforms previous state-of-the-art methods on three large-scale datasets: NTURGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400.
Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey
After analyzing previous related studies, a new taxonomy for skeleton-GNN-based methods is proposed according to their designs, and their merits and demerits are analyzed.
Big Data System for Dragon Boat Rowing Action Training Based on Multidimensional Stereo Vision
  • Sun Tao
  • Education
    Mathematical Problems in Engineering
  • 2022
With the rapid advancement of artificial intelligence technology and the widespread use of sensing technology in education, human-computer interaction teaching has gradually developed in sports and


Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
The proposed AS-GCN achieves consistently large improvement compared to the state-of-the-art methods and shows promising results for future pose prediction.
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
This paper introduces a global spatial aggregation scheme, which is able to learn superior joint co-occurrence features over local aggregation, and consistently outperforms other state-of-the-arts on action recognition and detection benchmarks like NTU RGB+D, SBU Kinect Interaction and PKU-MMD.
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning
A novel model with spatial reasoning and temporal stack learning (SR-TSL) for skeleton-based action recognition, which consists of a spatial reasoning network (SRN) and a temporal stacklearning network (TSLN).
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
A novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data.
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
This work proposes an end-to-end spatial and temporal attention model for human action recognition from skeleton data on top of the Recurrent Neural Networks with Long Short-Term Memory (LSTM), which learns to selectively focus on discriminative joints of skeleton within each frame of the inputs and pays different levels of attention to the outputs of different frames.
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
This paper introduces new gating mechanism within LSTM to learn the reliability of the sequential input data and accordingly adjust its effect on updating the long-term context information stored in the memory cell, and proposes a more powerful tree-structure based traversal method.
Skeleton-Based Action Recognition With Directed Graph Neural Networks
A novel directed graph neural network is designed specially to extract the information of joints, bones and their relations and make prediction based on the extracted features and is tested on two large-scale datasets, NTU-RGBD and Skeleton-Kinetics, and exceeds state-of-the-art performance on both of them.
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident.
Skeleton-based action recognition with convolutional neural networks
A novel convolutional neural networks (CNN) based framework for both action classification and detection of skeleton-based action recognition and a window proposal network to extract temporal segment proposals, which are further classified within the same network.
Hierarchical recurrent neural network for skeleton based action recognition
  • Yong DuWei WangLiang Wang
  • Computer Science
    2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2015
This paper proposes an end-to-end hierarchical RNN for skeleton based action recognition, and demonstrates that the model achieves the state-of-the-art performance with high computational efficiency.