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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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Cites
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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×
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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×
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BackgroundThe Kinetics Human Action Video DatasetPreprint
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BackgroundMethodInfluentialSelf-citationSpectral Networks and Locally Connected Networks on Graphs
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Cited by
Sorted by citations
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Wu, Z., Pan, S., Long, G., Jiang, J. B., Chang, X., & Chengqi Zhang, B. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403118 · Cited by 2,322
“For spatial-temporal graph neural networks, spatial dependencies among nodes are captured by graph convolutions, while temporal dependencies among historical states are preserved by recurrent neural networks [13, 17] or 1D convolutions [22, 23].”
BackgroundGraph convolutional networks: a comprehensive review
Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0069-y · Cited by 1,888
“In [81], a spatial-temporal graph convolutional model is designed to eliminate the need of hand-crafted part assignment and can achieve a greater expressive power.”
BackgroundLearning to Simulate Complex Physics with Graph NetworksPreprint
Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., & Battaglia, P. (2020) · International Conference on Machine Learning · doi:10.48550/arxiv.2002.09405 · Cited by 1,739
“…et al., 2019; Sanchez-Gonzalez et al., 2019), as well as non-physical systems, such as multi-agent dynamics (Tacchetti et al., 2018; Sun et al., 2019), algorithm execution (Velikovi et al., 2020), and other dynamic graph settings (Trivedi et al., 2019; 2017; Yan et al., 2018; Manessi et al., 2020).”
BackgroundDisentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
Liu, Z., Zhang, H., Chen, Z., Wang, Z., & Ouyang, W. (2020) · 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/cvpr42600.2020.00022 · Cited by 1,342
“In particular, skeleton-based human action recognition involves predicting actions from skeleton representations of human bodies instead of raw RGB videos, and the significant results seen in recent work [50, 33, 32, 34, 21, 20, 54, 35] have proven its merits.”
BackgroundMethodResultInfluentialMohamed, A. A., Qian, K., Elhoseiny, M., & Claudel, C. (2020) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.01443 · Cited by 1,100
“One of the development related to our work is the ST-GCNN [27].”
BackgroundDeep Learning-based Human Pose Estimation: A Survey
Zheng, C., Wu, W., Chen, C., Yang, T., Zhu, S., Shen, J., Kehtarnavaz, N., & Shah, M. A. (2023) · ACM Computing Surveys · doi:10.1145/3603618 · Cited by 934
“[275] leveraged the dynamic skeleton modality of pose for action recognition.”
BackgroundChannel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition
Chen, Y.-X., Zhang, Z.-Q., Yuan, C.-F., Li, B., Deng, Y., & Hu, W.-M. (2021) · IEEE International Conference on Computer Vision · doi:10.1109/iccv48922.2021.01311 · Cited by 1,057
“[31] proposed an ST-GCN which predefines topology according to human body structure and the topology is fixed in both training and testing phase.”
BackgroundMethodInfluentialSkeleton-Based Action Recognition With Shift Graph Convolutional Network
Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., & Lu, H. (2020) · 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/cvpr42600.2020.00026 · Cited by 1,046
“For example, ST-GCN [34] costs 16.”
BackgroundMethodInfluentialUnbiased Scene Graph Generation From Biased Training
Tang, K., Niu, Y., Huang, J., Shi, J., & Zhang, H. (2020) · 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/cvpr42600.2020.00377 · Cited by 810
“— yet on which are our core efforts made [69, 53, 6], then pretend that there is a graph — nothing but a sparse object layout with binary links, and finally shroud it into graph neural networks [63] for merely more contextual object representations [65, 17, 54].”
Human Action Recognition From Various Data Modalities: A Review
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 719
“Thus many recent works on skeleton-based HAR used skeleton data obtained from depth maps [151] or RGB videos [152].”
BackgroundMethodInfluentialRevisiting Skeleton-based Action Recognition
Duan, H., Zhao, Y., Chen, K., Lin, D., & Dai, B. (2022) · 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/cvpr52688.2022.00298 · Cited by 948
“Kinetics-400.”
BackgroundMethodInfluentialSelf-citationZhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
“To model the long-term temporal dependencies and dynamics, as well as spatial structures within the skeleton sequence, different neural networks have been exploited for action recognition, such as the deep recurrent neural network (RNN) [116], CNN [117], and deep graph convolutional networks (GCNs) [118].”
MethodLearning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting
Guo, S., Lin, Y., Wan, H., Li, X., & Cong, G. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3056502 · Cited by 800
“Depending on the choice of forward computation function, the STGNNs-based meth-ods can be divided into two categories, RNNs-based approaches [6], [7], [8] and CNNs-based approaches [9], [10], [11], [12].”
MethodInfluentialAction-Conditioned 3D Human Motion Synthesis with Transformer VAE
Petrovich, M., Black, M. J., & Varol, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01080 · Cited by 571
Vision GNN: An Image is Worth Graph of NodesPreprint
Han, K., Wang, Y.-H., Guo, J.-Y., Tang, Y.-H., & Wu, E. (2022) · Neural Information Processing Systems · doi:10.48550/arxiv.2206.00272 · Cited by 689
“By processing the naturally formed graph of linked human joints, GCN was utilized on human action recognition task [23, 62].”
MethodSTGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction
Huang, Y., Bi, H., Li, Z., Mao, T., & Wang, Z. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00637 · Cited by 697
“Recently, some meth-ods [27] [40] in the field of action recognition have made significant progress by applying GNNs to spatial-temporal”
BackgroundTraffic Flow Prediction via Spatial Temporal Graph Neural Network
Wang, X., Ma, Y., Wang, Y., Jin, W., Wang, X., Tang, J., Jia, C., & Yu, J. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380186 · Cited by 644
Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision
Wu, P., Liu, J., Shi, Y., Sun, Y., Shao, F., Wu, Z., & Yang, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_20 · Cited by 521
“For instance, GCN are used for temporal action localization [50], video classification [37,41], anomaly detection [51], skeleton-based action recognition [33,45], point cloud semantic segmentation [21], image captioning [46], and so on.”
MethodA Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances
Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., et al. (2022) · Information Fusion · doi:10.48550/arxiv.2203.06935 · Cited by 608
“In the combination of CNNs and RNNs, ConvNet-RNN networks with the cascaded fusion [230,283] or the ensemble strategy [215] have been proposed for FER, by simultaneously capturing textural and temporal information.”
MethodInfluentialLearning Trajectory Dependencies for Human Motion Prediction
Mao, W., Liu, M.-M., Salzmann, M., & Li, H. (2019) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2019.00958 · Cited by 591
“This strategy has been employed for action recognition [25], by using a GCN to capture the temporal and spatial dependencies of human joints via a graph defined on temporally connected kinematic trees.”
MethodSkeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks
Shi, L., Zhang, Y., Cheng, J., & Lu, H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3028207 · Cited by 638
Graph Convolutional Networks for Temporal Action Localization
Zeng, R., Huang, W., Gan, C., Tan, M., Rong, Y., Zhao, P., & Huang, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00719 · Cited by 570
“Thanks to its effectiveness, GCNs have been successfully applied to several research areas in computer vision, such as skeleton-based action recognition [47], person re-identification [32], and video classification [45].”
BackgroundMethodXia, F., Sun, K., Yu, S., Aziz, A., Wan, L., Pan, S., & Liu, H. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3076021 · Cited by 534
“studies in this direction have discussed the use of GCNs, the combination of GCNs with RNN or CNN, and recursive structures for graph structures [130], [131], [159].”
BackgroundMethodExploiting Spatial-Temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks
Cai, Y., Ge, L., Liu, J., Cai, J., Cham, T., Yuan, J., & Magnenat‐Thalmann, N. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00236 · Cited by 522
“To tackle this issue, inspired from the previous studies [33, 50] that take the convolutional operator with a larger kernel size, we made modifications to the generic graph convolutional operations.”
MethodA Comprehensive Survey of Vision-Based Human Action Recognition Methods
Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019) · Sensors · doi:10.3390/s19051005 · Cited by 503
FineGym: A Hierarchical Video Dataset for Fine-Grained Action Understanding
Shao, D., Zhao, Y., Dai, B., & Lin, D. (2020) · 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/cvpr42600.2020.00269 · Cited by 369
“3 ST-GCN [48] Pose 11.”
MethodSelf-citationConstructing Stronger and Faster Baselines for Skeleton-Based Action Recognition
Song, Y., Zhang, Z., Shan, C., & Wang, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3157033 · Cited by 563
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
Şener, F., Chatterjee, D., Shelepov, D., He, K., Singhania, D., Wang, R. Y., & Yao, A. (2022) · 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/cvpr52688.2022.02042 · Cited by 217
““2s-AGCN [41] w/ context” extends each boundary by 0.5 seconds; the extension improves action accuracy significantly.”
BackgroundInfluentialA Review of Graph Neural Networks and Their Applications in Power Systems
Liao, W., Bak‐Jensen, B., Pillai, J. R., Wang, Y., & Wang, Y. (2022) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2021.000058 · Cited by 491
“For example, a PGC layer and a one-dimensional convolutional layer are used to build spatial-temporal block in [55].”
MethodMotionBERT: A Unified Perspective on Learning Human Motion Representations
Zhu, W., Ma, X., Liu, Z., Liu, L., Wu, W., & Wang, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01385 · Cited by 416
“volutional Networks (ST-GCN) have been applied to modeling spatio-temporal relationship of human joints in both 3D pose estimation [13, 116] and action recognition [96, 123], but their connections have not been fully explored.”
BackgroundMethodInfluentialSkeleton-based action recognition via spatial and temporal transformer networks
Plizzari, C., Cannici, M., & Matteucci, M. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103219 · Cited by 409
“Finally, in Figure 5b we also compare the entire stream architectures, i.e., ST-GCN (Yan et al. (2018)) and 1s-AGCN (Shi et al. (2019b)) with the proposed S-TR and T-TR streams in terms of parameters.”
BackgroundMethodInfluentialMulti-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
Chen, Z., Li, S., Yang, B., Li, Q., & Liu, H. (2021) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16197 · Cited by 393
“ST-GCN (Yan, Xiong, and Lin 2018) 31.6 53.7 AS-GCN (Li et al. 2019a) 34.8 56.3 2s AGCN (Shi et al. 2019c) 36.1 58.7 4s DGNN (Shi et al. 2019a) 36.9 56.9 2s NAS-GCN (Peng et al. 2020) 37.1 60.1 2s MS-G3D (Liu et al. 2020) 38.0 60.9 Js”
MethodInfluentialSGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction
Shi, L., Wang, L., Long, C., Zhou, S., Zhou, M., Niu, Z., & Hua, G. (2021) · 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/cvpr46437.2021.00888 · Cited by 395
“To summarize, both Social-STGCNN and SGAN predict overlapping distributions and deviate from the ground-truth, while our predicted distributions exhibit less overlapping and have a better tendency along the ground-truth.”
BackgroundInfluentialDynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
Ye, F., Pu, S., Zhong, Q., Li, C., Xie, D., & Tang, H.-M. (2020) · ACM Multimedia · doi:10.1145/3394171.3413941 · Cited by 384
“We follow the same train-validation split as [33].”
BackgroundMethodInfluentialDynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction
Li, M., Chen, S.-H., Zhao, Y., Zhang, Y., Wang, Y.-F., & Tian, Q. (2020) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.00029 · Cited by 382
“Graph deep learning: Graphs, expressing data associated with non-grid structures, preserve the dependencies among internal nodes [46, 40, 39].”
BackgroundMethodInfoGCN: Representation Learning for Human Skeleton-based Action Recognition
Chi, H., Ha, M. H., Chi, S., Lee, S. W., Huang, Q., & Ramani, K. (2022) · 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/cvpr52688.2022.01955 · Cited by 498
“Since the introduction of GCN [21], various approaches exploiting the graph structure of extrinsic topology have been introduced [26, 33, 43, 56].”
BackgroundMethodInfluentialLearning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
Peng, W., Hong, X.-P., Chen, H., & Zhao, G.-Y. (2019) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5652 · Cited by 375
“To keep consistent with the current state-of-the-art GCN methods (Yan, Xiong, and Lin 2018; Shi et al. 2019) 2 , we introduce ten GCN blocks into our network for both searching and training steps.”
MethodResultInfluentialSong, Y., Zhang, Z., Shan, C., & Wang, L. (2020) · ACM Multimedia · doi:10.1145/3394171.3413802 · Cited by 363
“, ST-GCN [31], the PA-ResGCN achieves over 10% and 20% relative performance increases with the similar model size on the two datasets, NTU Frame n Frame n+1”
BackgroundMethodInfluentialH2O: Two Hands Manipulating Objects for First Person Interaction Recognition
Kwon, T., Tekin, B., Stühmer, J., Bogo, F., & Pollefeys, M. (2021) · IEEE International Conference on Computer Vision · doi:10.1109/iccv48922.2021.00998 · Cited by 356
“ST-GCN works on a single graph entity, e.g. human skeleton, and models intra-skeleton connections with a fixed adjacency matrix.”
BackgroundMethodInfluentialWhen physics meets machine learning: a survey of physics-informed machine learning
Meng, C., Griesemer, S., Cao, D., Seo, S., & Liu, Y. (2025) · Machine learning for computational science and engineering · doi:10.1007/s44379-025-00016-0 · Cited by 389
“Examples of real-world object-centric data include trajectory data from multi-agent sys-123 tems [114–116], position and velocity data from networks of motion sensors [115, 117], and molecule data [54].”
Hyperbolic Deep Neural Networks: A Survey
Peng, W., Varanka, T., Mostafa, A., Shi, H., & Zhao, G. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3136921 · Cited by 252
“Work [48] shares the same idea to construct spatial temporal graph convolutional networks [144] in the hyperbolic space and applied this for dynamic graph sequences.”
BackgroundGaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition
Teepe, T., Khan, A. R., Gilg, J. J., Herzog, F., Hörmann, S., & Rigoll, G. (2021) · International Conference on Information Photonics · doi:10.1109/icip42928.2021.9506717 · Cited by 324
“Inspired by the success of GCNs in skeleton-based action recognition [9, 10], we adapted the methods to the gait recognition task.”
MethodUnsupervised Domain Adaptation of Object Detectors: A Survey
Oza, P., Sindagi, V. A., VS, V., & Patel, V. M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3217046 · Cited by 313
Skeleton Aware Multi-modal Sign Language Recognition
Jiang, S., Sun, B., Wang, L., Bai, Y., Li, K., & Fu, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00380 · Cited by 309
Yadav, S. K., Tiwari, K., Pandey, H. M., & Akbar, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106970 · Cited by 340
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
Abadal, S., Jain, A., Guirado, R., López-Alonso, J., & Alarcón, E. (2021) · ACM Computing Surveys · doi:10.1145/3477141 · Cited by 310
Graph Stacked Hourglass Networks for 3D Human Pose Estimation
Xu, T., & Takano, W. (2021) · 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/cvpr46437.2021.01584 · Cited by 312
“Moreover, our hourglass module can be seen as a wellintegrated, high-performance graph convolution module, indicating that our hourglass module is general and can be easily extended to other tasks using graph convolution, such as action recognition [46, 20], motion prediction [21], etc.”
MethodHierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition
Lee, J., Lee, M., Lee, D., & Lee, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00958 · Cited by 333
“However, existing GCN-based methods [35, 24, 25, 4, 2] have the following limitations.”
BackgroundMethodInfluentialGraph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294
“Specifically, the inductive ability of graph convolutional networks (GCNs) to generalize to data points unseen during model training, and to generate new data points from scratch by decoding latent representation from the embedding space, has enabled the discovery of new molecules, interactions and functions 100,105,1…”
InfluentialGraph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
Ahmedt‐Aristizabal, D., Armin, M. A., Denman, S., Fookes, C., & Petersson, L. (2021) · Sensors · doi:10.3390/s21144758 · Cited by 298
“ST-GCN model: ST-GCN are popular for solving problems that base predictions on graph-structured time series [63].”
BackgroundMethodUAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles
Li, T., Liu, J., Zhang, W., Ni, Y., Wang, W., & Li, Z. (2021) · 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/cvpr46437.2021.01600 · Cited by 309
“Here we evaluate different state-of-the-art methods [39, 31, 30, 19, 6] on skeleton modality of the UAV-Human.”
MethodMSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction
Dang, L., Nie, Y.-W., Long, C., Zhang, Q., & Li, G.-Q. (2021) · IEEE International Conference on Computer Vision · doi:10.1109/iccv48922.2021.01127 · Cited by 284
“, biological gene, point cloud, human social network [48], and human motion prediction for the graph-structure nature of the human skeleton.”
BackgroundCross-Domain Detection via Graph-Induced Prototype Alignment
Xu, M., Wang, H., Ni, B., Tian, Q. C., & Zhang, W. (2020) · 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/cvpr42600.2020.01237 · Cited by 274
“, action recognition [50], person Re-ID [51], video understanding [45, 52] and point cloud learning [23].”
BackgroundSpatio-Temporal Graph for Video Captioning With Knowledge Distillation
Pan, B., Cai, H., Huang, D.-A., Lee, K.-H., Gaidon, A., Adeli, E., & Niebles, J. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01088 · Cited by 281
“We follow [47] to add in the residual connection and use ReLU as the activation function.”
BackgroundMethodSpatiotemporal Co-Attention Recurrent Neural Networks for Human-Skeleton Motion Prediction
Shu, X., Zhang, L., Qi, G.-J., Liu, W., & Tang, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3050918 · Cited by 276
“skeleton [21], [33], [34], [35], [36], [37].”
MethodSpatial Temporal Transformer Network for Skeleton-based Action Recognition
Plizzari, C., Cannici, M., & Matteucci, M. (2020) · ICPR Workshops · doi:10.1007/978-3-030-68796-0_50 · Cited by 274
“ST-GCN [16] 85.”
MethodInfluentialPREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition
Su, K., Liu, X., & Shlizerman, E. (2020) · 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/cvpr42600.2020.00965 · Cited by 244
“In particular, Spatio-Temporal GCN was designed to learn both spatial and temporal patterns from skeleton data [33].”
MethodHuman activity recognition in artificial intelligence framework: a narrative review
Gupta, N., Gupta, S. K., Pathak, R. K., Jain, V., Rashidi, P., & Suri, J. S. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10116-x · Cited by 360
“2020), NTU-RGBD datasets (Yan et al. 2018; Liu et al. 2016), VIRAT 1.”
Spatio-Temporal Graph Routing for Skeleton-Based Action Recognition
Li, B., Li, X., Zhang, Z. M., & Wu, F. Q. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018561 · Cited by 271
“ST-GCN takes both 3D skeleton and the graph as input and output action class.”
MethodInfluentialDrive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles
Martín, M., Roitberg, A., Haurilet, M., Horne, M., Reiß, S., Voit, M., & Stiefelhagen, R. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00289 · Cited by 213
“Due to the complex structure of skeletons, popular ways for flattening their representation include recurrent neural networks [32, 28] and graph networks [51].”
BackgroundHow to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
Ye, J., Zhao, J., Ye, K., & Xu, C. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3043250 · Cited by 298
“An evolving topological structure [66], [114] is incorporated into the model to capture such dynamic spatial change.”
BackgroundMotion Guided 3D Pose Estimation from Videos
Wang, J., Yan, S.-J., Xiong, Y., & Lin, D.-H. (2020) · European Conference on Computer Vision · doi:10.1007/978-3-030-58601-0_45 · Cited by 257
“Therefore, we represent the skeleton sequence as a spatial temporal graph [38] to maintain their topologies, and aggregating information by an U-shaped graph convolution network (UGCN).”
BackgroundMethodInfluentialSelf-citationGraph Neural Ordinary Differential EquationsPreprint
Poli, M., Massaroli, S., Park, J., Yamashita, A., Asama, H., & Park, J. (2019) · arXiv.org · doi:10.48550/arxiv.1911.07532 · Cited by 256
“GNNs have shown remarkable results in various application areas such as node classification (Atwood and Towsley 2016), graph classification (Yan, Xiong, and Lin 2018) and forecasting (Li et al. 2017; Park and Park 2019) as well as generative tasks (Li et al. 2018).”
BackgroundPYSKL: Towards Good Practices for Skeleton Action Recognition
Duan, H., Wang, J., Chen, K., & Lin, D. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548546 · Cited by 216
Self-citationHOPE-Net: A Graph-Based Model for Hand-Object Pose Estimation
Doosti, B., Naha, S., Mirbagheri, M., & Crandall, D. J. (2020) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.00664 · Cited by 241
“[29] used a graph convolution network for learning a spatial-temporal representation of human body joints for skeleton-based action recognition.”
MethodSymbiotic Graph Neural Networks for 3D Skeleton-Based Human Action Recognition and Motion Prediction
Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., & Tian, Q. C. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3053765 · Cited by 243
“Here we compare Sybio-GNN with a few recently proposed methods: ST-GCN [12], HCN [9], and 2s-AGCN [35].”
BackgroundMethodInfluentialA Comprehensive Study of Deep Video Action RecognitionPreprint
Zhu, Y., Li, X., Liu, C., Zolfaghari, M., Xiong, Y., Wu, C., Zhang, Z., Tighe, J., Manmatha, R., & Li, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06567 · Cited by 212
“Although there is previous work on using pose for action recognition [150, 246], P-CNN [23] was one of the first deep learning methods that successfully used pose to improve video action recognition.”
MethodSelf-citationZ-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series ForecastingPreprint
Chen, Y., Segovia-Domínguez, I., & Gel, Y. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04100 · Cited by 96
“In addition to convolution, there has been a surge of interest in applying GCNs to time series forecasting tasks (Yu et al., 2018; Yao et al., 2018; Yan et al., 2018; Guo et al., 2019; Weber et al., 2019; Pareja et al., 2020; Segovia Dominguez et al., 2021).”
BackgroundBoulahia, S. Y., Amamra, A., Madi, M. R., & Daikh, S. (2021) · Machine Vision and Applications · doi:10.1007/s00138-021-01249-8 · Cited by 236
“For instance, the use of the ST-GCN [41] extraction module provides on average 96, 000 features for each skeleton-based action sample.”
MethodInfluential3D Human Action Representation Learning via Cross-View Consistency Pursuit
Li, L., Wang, M., Ni, B., Wang, H., Yang, J., & Zhang, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00471 · Cited by 236
“Further, ST-GCN [57] was proposed to better model the graph structure of skeleton data.”
MethodInfluentialRao, H., Xu, S., Hu, X., Cheng, J., & Hu, B. (2021) · Information Sciences · doi:10.1016/j.ins.2021.04.023 · Cited by 235
“Most existing skeleton-based methods [9]–[11] utilize the supervised learning paradigm to learn action representations, in which a significant number of annotations for action frames or videos is indispensable.”
BackgroundMethodInfluentialHiGCIN: Hierarchical Graph-Based Cross Inference Network for Group Activity Recognition
Yan, R., Xie, L.-X., Tang, J.-H., Shu, X.-B., & Tian, Q. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3034233 · Cited by 234
“Moreover, some STGNNs [41], [42], [43], [44] have been designed to learn unseen patterns from spatiotemporal graph data (e.g., skeleton-based actions) by combing a convolution and a recurrent operation.”
BackgroundA Survey on 3D Skeleton-Based Action Recognition Using Learning Method
Ren, B., Liu, M., Ding, R., & Liu, H. (2024) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0100 · Cited by 236
“Nowadays, deep learning techniques utilizing recurrent neural networks (RNNs) [51], convolutional neural networks (CNNs) [52], graph convolutional networks (GCNs), and Transformer-based methods have emerged in this field [53,54].”
BackgroundRichly Activated Graph Convolutional Network for Robust Skeleton-Based Action Recognition
Song, Y., Zhang, Z., Shan, C., & Wang, L. (2020) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2020.3015051 · Cited by 231
“Compared to STGCN [10], which is the first graph-based model for skeleton action recognition, our method outperforms by 5.8% and 5.3%, respectively.”
BackgroundMethodInfluentialSkeleton-aware networks for deep motion retargeting
Aberman, K., Li, P., Lischinski, D., Sorkine-Hornung, O., Cohen-Or, D., & Chen, B. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392462 · Cited by 230
Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal AttentionPreprint
Kim, B.-H., Ye, J. C., & Kim, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.13495 · Cited by 116
“Experiment on other models including ST-GCN [15], GIN [23], GCN [24], GraphSAGE [31], and ChebGCN [2] demonstrate exceptional performance of our proposed method for HCP-Task (Table 1).”
MethodInfluentialGraph Neural Architecture Search
Gao, Y., Yang, H., Zhang, P., Zhou, C., & Hu, Y. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/195 · Cited by 226
“The applications of GNNs span over online recommendation [Wu et al. , 2019b], traffic forecasting [Yu et al. , 2018] and action recognition [Yan et al. , 2018].”
BackgroundDeepGCNs: Making GCNs Go as Deep as CNNs
Li, G., Mueller, M. F., Qian, G., Delgadillo, I. C., Abualshour, A., Thabet, A., & Ghanem, B. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3074057 · Cited by 229
“Graphs are also used to model human joints for action recognition in video [23], [24].”
MethodMS2L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
Lin, L., Song, S., Yang, W., & Liu, J. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413548 · Cited by 223
“30 ST-GCN [43] 81.”
BackgroundMethodInfluentialKim, J., Kim, H., Kim, H., Lee, D., & Yoon, S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11223-9 · Cited by 389
Music Gesture for Visual Sound Separation
Gan, C., Huang, D. Y., Zhao, H., Tenenbaum, J. B., & Torralba, A. B. (2020) · 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/cvpr42600.2020.01049 · Cited by 197
“Similar to [53], we start by constructing a undirected spatial-temporal graph G = {V,E} on a human skeleton sequence.”
MethodSpatio-Temporal Graph Convolution for Resting-State fMRI Analysis
Gadgil, S. U., Zhao, Q., Adolf Pfefferbaum, A., Sullivan, E. V., Adeli, E., & Pohl, K. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59728-3_52 · Cited by 215
“To address these issues, we suggest deep neural networks to incorporate spatio-temporal convolution on functional connectivity graphs, i.e., spatio-temporal Graph Convolution (ST-GC) [24,25].”
BackgroundMethodInfluentialGraph Embedded Pose Clustering for Anomaly Detection
Markovitz, A., Sharir, G., Friedman, I., Zelnik-Manor, L., & Avidan, S. (2019) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.01055 · Cited by 222
“[27] and Yu et al .”
BackgroundMethodInfluentialClass-Incremental Continual Learning Into the eXtended DER-Verse
Boschini, M., Bonicelli, L., Buzzega, P., Porrello, A., & Calderara, S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3206549 · Cited by 133
“We read the raw data as done by the official codebase for [76], which represents each sample as a S × T × J × B tensor, where S = 3 is the number of dimensions in space, T = 300 is the number of frames per sequence, J = 25 is the number of tracked skeletal compute the rescaling factor M ← save new…”
CATER: A diagnostic dataset for Compositional Actions and TEmporal ReasoningPreprint
Girdhar, R., & Ramanan, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.04744 · Cited by 116
Space-Time-Separable Graph Convolutional Network for Pose Forecasting
Sofianos, T., Sampieri, A., Franco, L., & Galasso, F. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01102 · Cited by 210
“These have mostly been hand-designed, mainly leveraging the natural structure of the kinematic tree [25, 8, 51], and encoded via Graph Convolu-tional Networks (GCN) [27].”
InfluentialAdaptive Context-Aware Multi-Modal Network for Depth Completion
Zhao, S., Gong, M., Fu, H., & Tao, D. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3079821 · Cited by 194
“To model the graph data efficiently, Graph Models have been applied on various computer vision tasks, such as action recognition [64, 65], point cloud analysis [66, 23], few-shot image classification [67, 68], and person reidentification [69].”
MethodUncertainty-Aware Score Distribution Learning for Action Quality Assessment
Tang, Y., Ni, Z., Zhou, J., Zhang, D., Lu, J., Wu, Y., & Zhou, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00986 · Cited by 209
“ST-GCN [16,38] 0.”
Graph Neural Networks: Taxonomy, Advances, and Trends
Zhou, Y. P., Zheng, H., Huang, X., Hao, S., Li, D., & Zhao, J. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3495161 · Cited by 178
“Spatio-temporal graphs can be used to model traffic networks [59, 71] and skeleton networks [55, 57].”
MethodMulti-Label Classification with Label Graph Superimposing
Wang, Y., He, D.-L., Li, F., Long, X., Zhou, Z., Ma, J.-W., & Wen, S.-L. (2019) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6909 · Cited by 208
“For instance, it was leveraged in (Yan, Xiong, and Lin 2018; Gao et al. 2018) to model the relationship of skeletons of humans bodies for human action recognition and knowledge-aware GCN was applied for zero-shot video classification in (Gao, Zhang, and Xu 2019).”
MethodDiffPose: Toward More Reliable 3D Pose Estimation
Gong, J. L., Foo, L. G., Fan, Z., Ke, Q., Rahmani, H., & Liu, J. (2023) · 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/cvpr52729.2023.01253 · Cited by 208
“Unlike these generation tasks [20, 25] that often start the generation process from random noise, our pose estimation process starts from an estimated 2D pose with uncertainty and indeterminacy in 3D space, where the uncertainty distribution differs for each pose and can also be irregular and difficult to characterize.”
BackgroundChen, C., Wu, Y., Dai, Q., Zhou, H.-Y., Xu, M., Yang, S., Han, X., & Yu, Y. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3445463 · Cited by 198
“Considering the human skeleton has a graph structure with natural joint connections, Yan et al. [99] propose an ST-GCN network that first connects joints in the same frame according to the natu-ral connectivity in the human skeleton, and then connects the same joints in two consecutive frames to…”
BackgroundMethodInfluentialSpatial-Temporal Graph Convolutional Network for Video-Based Person Re-Identification
Yang, J., Zheng, W., Yang, Q., Chen, Y.-C., & Tian, Q. (2020) · 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/cvpr42600.2020.00335 · Cited by 260
“In recent years, graph convolutional networks (GCNs) and its variants [20, 5, 14, 18] have been successfully applied to some tasks in computer vision, such as skeleton-based action recognition [62], video classication [52], and multi-label image recognition [7] due to its strong ability of relations modeling.”
BackgroundCross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
Meng, C., Rambhatla, S., & Liu, Y. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467371 · Cited by 156
“sensors, wearable devices and the Internet of Things (IoT) devices) is critical for various applications including traffic flow prediction [18, 32], forecasting [4, 24], and user activity detection [20, 29].”
BackgroundKumar, P., Chauhan, S., & Awasthi, L. K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09986-x · Cited by 228
“Many GNN and GCN-based HAR methods [207, 208] have been proposed because the skeleton data can be represented as a graph with edges and nodes.”
Improving Action Segmentation via Graph-Based Temporal Reasoning
Huang, Y., Sugano, Y., & Sato, Y. (2020) · 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/cvpr42600.2020.01404 · Cited by 160
“Since then, GCNs have shown convincing successes in modeling relations [2, 15, 22] and thus are widely applied in multiple research tasks like semisupervised learning [38], image captioning [63], skeletonbased action recognition [62], and video action recognition [58, 60, 65, 66].”
BackgroundShaik, T., Tao, X., Li, L., Xie, H., & Velasquez, J. D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102040 · Cited by 228
“Graph-based algorithms, such as graph convolutional networks (GCNs) or graph regularized non-negative matrix factorization (GNMF), can then be applied to capture the dependencies and interactions between modalities [97].”
MethodSignBERT+: Hand-Model-Aware Self-Supervised Pre-Training for Sign Language Understanding
Hu, H., Zhao, W., Zhou, W., & Li, H. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3269220 · Cited by 167
“Spatially, following [24], we adopt random moving augmentation to simulate spatial disturbance induced by rotation, translation and scaling factors.”
MethodInfluential
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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