The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index.
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Open accessAbstract
Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we propose 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. This formulation not only leads to greater expressive power but also stronger generalization capability. On two large datasets, Kinetics and NTU-RGBD, it achieves substantial improvements over mainstream methods.
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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · cited 1×
“In tasks such as image object recognition, the hierarchical representation and locality are usually achieved by the intrinsic properties of convolutional neural networks (Krizhevsky, Sutskever, and Hinton 2012), rather than manually assigning object parts.”
MethodSemi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · cited 8×
“In the single frame case, ST-GCN with the first partitioning strategy can be implemented with the following formula (Kipf and Welling 2017) where Here the weight vectors of multiple output channels are stacked to form the weight matrix W .”
BackgroundMethodInfluentialLearning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593 · cited 2×
“In general, human action can be recognized from multiple modalities(Simonyan and Zisserman 2014; Tran et al. 2015; Wang, Qiao, and Tang 2015; Wang et al. 2016; Zhao et al. 2017), such as appearance, depth, optical flows, and body skeletons (Du, Wang, and Wang 2015; Liu et al.”
BackgroundRealtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539 · cited 3×
“Skeleton and joint trajectories of human bodies are robust to illumination change and scene variation, and they are easy to obtain owing to the highly accurate depth sensors or pose estimation algorithms (Shotton et al. 2011; Cao et al. 2017a).”
BackgroundMethodDeformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513 · cited 1×
“More detailed explanation and other applications of this formulation can be found in (Dai et al. 2017).”
BackgroundSelf-citationAdvances In Deep Learning On Graphs (Gsp'18 Workshop)Preprint
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 7,021 · cited 2×
“…graphs of arbitrary structures, have received increasing attention and successfully been adopted in a number of applications, such as image classification (Bruna et al. 2014), document classifi-cation (Defferrard, Bresson, and Vandergheynst 2016), and semi-supervised learning (Kipf and Welling 2017).”
BackgroundTwo-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756 · cited 1×
“In general, human action can be recognized from multiple modalities(Simonyan and Zisserman 2014; Tran et al. 2015; Wang, Qiao, and Tang 2015; Wang et al. 2016; Zhao et al. 2017), such as appearance, depth, optical flows, and body skeletons (Du, Wang, and Wang 2015; Liu et al. 2016).”
BackgroundThe Kinetics Human Action Video DatasetPreprint
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.06950 · Cited by 5,004 · cited 13×
“Weevaluate the recognition performance by top-1 and top-5 classification accuracy as recommended by the dataset authors (Kay et al. 2017).”
BackgroundMethodInfluentialTemporal Segment Networks: Towards Good Practices for Deep Action Recognition
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“We train the standard TSN (Wang et al. 2016) models from scratches on Kinetics with RGB and optical flow models.”
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Gated Graph Sequence Neural NetworksPreprint
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“The discussed neural network architectures include both recurrent neural networks (Tai, Socher, and Manning 2015; Van Oord, Kalchbrenner, and Kavukcuoglu 2016) and convolutional neural networks (CNNs) (Bruna et al. 2014; Henaff, Bruna, and LeCun 2015; Duvenaud et al. 2015; Li et al. 2016; Defferrard, Bresson, and Vand…”
BackgroundInfluentialNTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Shahroudy, A., Liu, J., Ng, T.-T., & Wang, G. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.115 · Cited by 3,148 · cited 9×
“All rights reserved. attempt to leverage the natural connections between joints have been developed (Shahroudy et al. 2016; Du, Wang, and Wang 2015).”
BackgroundMethodInfluentialImproved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Tai, K. S., Socher, R., & Manning, C. D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1150 · Cited by 3,081
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628 · cited 2×
“…spectral perspective , where the locality of the graph convolution is considered in the form of spectral analysis (Henaff, Bruna, and Le-Cun 2015; Duvenaud et al. 2015; Li et al. 2016; Kipf and Welling 2017); 2) the spatial perspective , where the convolution filters are applied directly on the…”
BackgroundLearning Convolutional Neural Networks for GraphsPreprint
Niepert, M., Ahmed, M., & Kutzkov, K. (2016) · International Conference on Machine Learning · doi:10.48550/arxiv.1605.05273 · Cited by 2,315 · cited 2×
“The solution to this problem is first investigated in (Niepert, Ahmed, and Kutzkov 2016), where the order is defined by a graph labeling process in the neighbor graph around the root node.”
BackgroundMethodReal-time human pose recognition in parts from single depth images
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A. W., Finocchio, M., Blake, A., Cook, M., & Moore, R. (2013) · Communications of the ACM · doi:10.1145/2398356.2398381 · Cited by 2,050
Hierarchical recurrent neural network for skeleton based action recognition
Du, Y., Wang, W., & Wang, L. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298714 · Cited by 2,012 · cited 2×
“All rights reserved. attempt to leverage the natural connections between joints have been developed (Shahroudy et al. 2016; Du, Wang, and Wang 2015).”
BackgroundPixel Recurrent Neural NetworksPreprint
van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.06759 · Cited by 1,793
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
Vemulapalli, R., Arrate, F., & Chellappa, R. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.82 · Cited by 1,506
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
Liu, J., Shahroudy, A., Xu, D., & Wang, G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_50 · Cited by 1,369 · cited 3×
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BackgroundMethodDeep Convolutional Networks on Graph-Structured Data
Henaff, M., Bruna, J., & LeCun, Y. (2015) · arXiv · doi:10.48550/arxiv.1506.05163 · Cited by 1,255
Action recognition with trajectory-pooled deep-convolutional descriptors
Wang, L., Qiao, Y., & Tang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299059 · Cited by 1,194
Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., & Xie, X. S. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10451 · Cited by 958 · cited 2×
“These works have been using recurrent neural networks (Shahroudy et al. 2016; Zhu et al. 2016; Liu et al. 2016; Zhang, Liu, and Xiao 2017) and temporal CNNs (Li et al.”
BackgroundA New Representation of Skeleton Sequences for 3D Action Recognition
Ke, Q., Bennamoun, M., An, S., Sohel, F., & Boussaid, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.486 · Cited by 793 · cited 10×
“Our ST-GCN is the first to apply graph CNNs to the task of skeleton based action recognition.”
BackgroundMethodInfluentialInterpretable 3D Human Action Analysis with Temporal Convolutional Networks
Kim, T. S., & Reiter, A. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.207 · Cited by 707
Modeling video evolution for action recognition
Fernando, B., Gavves, E., Jose Oramas, M., Ghodrati, A., & Tuytelaars, T. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299176 · Cited by 573 · cited 2×
“The first is the feature encoding approach on hand-crafted features (Fernando et al. 2015), referred to as “Feature Encoding” in Table 2.”
MethodDifferential Recurrent Neural Networks for Action Recognition
Veeriah, V., Zhuang, N., & Qi, G.-J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.460 · Cited by 531
Skeleton-based action recognition with convolutional neural networks
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“Our ST-GCN is the first to apply graph CNNs to the task of skeleton based action recognition.”
BackgroundInfluentialOn Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks
Zhang, S., Liu, X., & Xiao, J. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.24 · Cited by 322
Temporal Action Detection with Structured Segment Networks
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Flagged first, then by citations
1 of the 32 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
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A Comprehensive Survey on Graph Neural Networks
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Background
Metadata
- Authors
- Yan, S., Xiong, Y., & Lin, D.
- Affiliations
- Chinese University of Hong Kong (HK)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-27
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2963076818
- Cited by
- 5,410
- References
- 32
- Citations by year
- 2026: 552 · 2025: 895 · 2024: 915 · 2023: 915 · 2022: 755 · 2021: 742 · 2020: 414 · 2019: 193 · 2018: 29
- FWCI
- 103.84
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Chinese University of Hong Kong
- SenseTime Group
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Human Motion and Animation
- Keywords
- skeleton-based action recognition, spatio-temporal graph convolutional network, graph convolutional networks, temporal modeling, spatial modeling, generalization
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