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Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition [article]

Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian
2019 arXiv   pre-print
Combing the two types of links into a generalized skeleton graph, we further propose the actional-structural graph convolution network (AS-GCN), which stacks actional-structural graph convolution and temporal  ...  convolution as a basic building block, to learn both spatial and temporal features for action recognition.  ...  Conclusions We propose the actional-structural graph convolution networks (AS-GCN) for skeleton-based action recognition. The A-link inference module captures actional dependencies.  ... 
arXiv:1904.12659v1 fatcat:dedz5p3ywja5tdln5eizpzdjva

Structure-Feature Fusion Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition

Zhitao Zhang, Zhengyou Wang, Shanna Zhuang, Fuyu Huang
2020 IEEE Access  
SKELETON-BASED ACTION RECOGNITION Traditional methods of skeleton-based action recognition mainly focus on hand-crafted features.  ...  (RNNs), convolution neural networks (CNNs) or graph convolutional networks(GCNs).  ... 
doi:10.1109/access.2020.3046142 fatcat:xnk4hwfxxbeoffcnfcyvovqhaq

Multi-filter dynamic graph convolutional networks for skeleton-based action recognition

Yating Yuan, Bo Yu, Wei Wang, Bihui Yu
2021 Procedia Computer Science  
proposes an Inception structure and dynamic skeleton diagram based on the convolutional neural network, namely, the multi-filter dynamic graph convolutional neural network.  ...  proposes an Inception structure and dynamic skeleton diagram based on the convolutional neural network, namely, the multi-filter dynamic graph convolutional neural network.  ...  The title of the project is Research on voice print recognition and Character Action Recognition of Network Culture service.  ... 
doi:10.1016/j.procs.2021.02.099 fatcat:sbws5wfz7bamfomqeaqhbmtvny

Deep learning‐based action recognition with 3D skeleton: A survey

Yuling Xing, Jia Zhu
2021 CAAI Transactions on Intelligence Technology  
In this survey, we first introduce the development process of 3D skeleton-data action recognition and the classification of graph convolutional network, then introduce the commonly used NTU RGB + D and  ...  Action recognition based on 3D skeleton data has attracted much attention due to its wide application, and it is one of the most popular research topics in computer vision.  ...  [10] proposed the actional-structural graph convolution network (AS-GCN) by generating the skeleton graph with actional links and structural links.  ... 
doi:10.1049/cit2.12014 fatcat:77vtqpozjnfdrj3qodcbjzgjmq

Multi-scale Mixed Dense Graph Convolution Network for Skeleton-based Action Recognition

Hailun Xia, Xinkai Gao
2021 IEEE Access  
In skeleton-based action recognition, the approaches based on graph convolutional networks(GCN) have achieved remarkable performance by modeling spatial-temporal graphs to explore the physical dependencies  ...  INDEX TERMS Dense graph convolution, spatial and temporal attention module, multi-scale mixed temporal convolution, skeleton-based action recognition.  ...  Convolutional neural networks also achieve remarkable results for skeleton-based action recognition.  ... 
doi:10.1109/access.2020.3049029 fatcat:xlmmcsmp3vbnvj422wctwwjiei

Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition [article]

Jinfeng Wei, Yunxin Wang, Mengli Guo, Pei Lv, Xiaoshan Yang, Mingliang Xu
2021 arXiv   pre-print
Graph convolutional networks (GCNs) based methods have achieved advanced performance on skeleton-based action recognition task.  ...  In this work, we propose a novel dynamic hypergraph convolutional networks (DHGCN) for skeleton-based action recognition.  ...  stream adaptive graph convolutional networks for skeleton-based action recognition.  ... 
arXiv:2112.10570v1 fatcat:x2564jl4dngmhba7duxf5yai6m

Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition [article]

Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu
2019 arXiv   pre-print
In this work, we propose a novel two-stream adaptive graph convolutional network (2s-AGCN) for skeleton-based action recognition.  ...  In skeleton-based action recognition, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have achieved remarkable performance.  ...  strategies for skeleton-based action recognition.  ... 
arXiv:1805.07694v3 fatcat:5ev3nthavngg3pbiobdfryngz4

Multi‐stream adaptive spatial‐temporal attention graph convolutional network for skeleton‐based action recognition

Lubin Yu, Lianfang Tian, Qiliang Du, Jameel Ahmed Bhutto
2021 IET Computer Vision  
Graph convolutional networks (GCNs) generalize convolutional neural networks (CNNs) to non-Euclidean graphs and achieve significant performance in skeleton-based action recognition.  ...  Skeleton-based action recognition algorithms have been widely applied to human action recognition.  ...  [17] first applied GCNs to action recognition based on skeleton structures.  ... 
doi:10.1049/cvi2.12075 fatcat:auyz7ymr3fevhphfk32qzwmqae

Improved GCN Framework for Human Motion Recognition

Fen Zhou, Xuping Tu, Qingdong Wang, Guosong Jiang
2022 Scientific Programming  
Human recognition models based on spatial-temporal graph convolutional neural networks have been gradually developed, and we present an improved spatial-temporal graph convolutional neural network to solve  ...  It overcomes the shortcomings of temporal graph convolutional networks in the field of joint relevance of hidden layers and compensates for the information omission of small-scale graph tasks.  ...  Basic Network rough our preliminary examination, we apply the graph convolutional neural network as the base network, and its network structure is shown in Figure 1 . is network is an upgrade for the  ... 
doi:10.1155/2022/2721618 doaj:bc25bb3130c1468a89bf6e1c62af3607 fatcat:alds5mvnvva4pj74rbxjxlxoyu

Whole and Part Adaptive Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition

Qi Zuo, Lian Zou, Cien Fan, Dongqian Li, Hao Jiang, Yifeng Liu
2020 Sensors  
Spatiotemporal graph convolution has made significant progress in skeleton-based action recognition in recent years.  ...  and the part graph convolution network (PGCN).  ...  We propose two new graph convolution methods for skeleton-based action recognition based on the movement patterns of human action: whole graph convolution (WGCN) and part graph convolution (PGCN), which  ... 
doi:10.3390/s20247149 pmid:33322231 fatcat:dgvzytru6rdfjb4ypbqhc3nika

Centrality Graph Convolutional Networks for Skeleton-based Action Recognition [article]

Dong Yang, Monica Mengqi Li, Hong Fu, Jicong Fan, Howard Leung
2020 arXiv   pre-print
The topological structure of skeleton data plays a significant role in human action recognition.  ...  Combining the topological structure with graph convolutional networks has achieved remarkable performance.  ...  Related work Skeleton-based action recognition.  ... 
arXiv:2003.03007v1 fatcat:k4pe77g5abh4vakzrdq7wb4nhi

Dual Attention-Guided Multiscale Dynamic Aggregate Graph Convolutional Networks for Skeleton-Based Human Action Recognition

Zeyuan Hu, Eung-Joo Lee
2020 Symmetry  
In this paper, we propose a dual attention-guided multiscale dynamic aggregate graph convolution neural network (DAG-GCN) for skeleton-based human action recognition.  ...  Traditional convolution neural networks have achieved great success in human action recognition.  ...  [18] proposed a novel structure-induced graph convolutional network (Si-GCN) framework to enhance the performance of the skeleton-based action recognition task. Li F et al.  ... 
doi:10.3390/sym12101589 fatcat:t23qivoaujdgfi3yn2ff2xfqtq

GAS-GCN: Gated Action-Specific Graph Convolutional Networks for Skeleton-Based Action Recognition

Wensong Chan, Zhiqiang Tian, Yang Wu
2020 Sensors  
Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs).  ...  In this paper, we propose an action-specific graph convolutional module, which is able to extract the implicit connections and properly balance them for each action.  ...  network for skeleton-based action recognition.  ... 
doi:10.3390/s20123499 pmid:32575802 fatcat:aczc3nt3ivetxnztys27i5q77y

On the spatial attention in Spatio-Temporal Graph Convolutional Networks for skeleton-based human action recognition [article]

Negar Heidari, Alexandros Iosifidis
2021 arXiv   pre-print
Graph convolutional networks (GCNs) achieved promising performance in skeleton-based human action recognition by modeling a sequence of skeletons as a spatio-temporal graph.  ...  Most of the recently proposed GCN-based methods improve the performance by learning the graph structure at each layer of the network using a spatial attention applied on a predefined graph Adjacency matrix  ...  The European Commission is not responsible for any use that may be made of the information it contains.  ... 
arXiv:2011.03833v2 fatcat:6my7yxt3hvg5pkj4t7e5xamfr4

Remarkable Skeleton Based Human Action Recognition

Sushma Jaiswal, Tarun Jaiswal
2020 Artificial Intelligence Evolution  
Skeleton-based human-action-recognition (SBHAR) has wide applications in cognitive science and automatic surveillance.  ...  In this paper, we first highlight the need for action recognition and significance of 3D skeleton data and finally, we survey the largest 3D skeleton dataset, i.e.  ...  The new method Attention enhanced Graph Convolutional LSTM Network (AGC-LSTM) is introduced for action-recognition [43] .  ... 
doi:10.37256/aie.122020562 fatcat:2wdzis5ax5bdfnwdhlzgvoh6xu
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