Fetching the paper…
Reading the bibliography…
Skeleton Action Recognition (SAR) involves identifying human actions using skeletal joint coordinates and their interconnections.
Kalman, R.E.: A new approach to linear filtering and prediction problems (1960)
1960
Earlier work this paper cites.
Zhang, Z.: Microsoft kinect sensor and its effect. IEEE MultiMedia 19
2012
Earlier work this paper cites.
Wang, J., Nie, X., Xia, Y., Wu, Y., Zhu, S.C.: Cross-view action modeling, learning and recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2649–2656 (2014)
2014
Earlier work this paper cites.
Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1110–1118 (2015), https://api.semanticscholar.org/CorpusID:8040013
2015
Earlier work this paper cites.
Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2015)
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
Veeriah, V., Zhuang, N., Qi, G.J.: Differential recurrent neural networks for action recognition. In: Proceedings of the IEEE international conference on computer vision. pp. 4041–4049 (2015)
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: Ntu rgb+d: A large scale dataset for 3d human activity analysis. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1010–1019 (2016). https://doi.org/10.1109/CVPR.2016.115
2016
Earlier work this paper cites.
Song, S., Lan, C., Xing, J., Zeng, W., Liu, J.: An end-to-end spatio-temporal attention model for human action recognition from skeleton data. Proceedings of the AAAI Conference on Artificial Intelligence 31
2016
Earlier work this paper cites.
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1302–1310 (2017). https://doi.org/10.1109/CVPR.2017.143
2017
Earlier work this paper cites.
Ke, Q., Bennamoun, M., An, S., Sohel, F., Boussaid, F.: A new representation of skeleton sequences for 3d action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3288–3297 (2017)
2017
Earlier work this paper cites.
Lee, I., Kim, D., Kang, S., Lee, S.: Ensemble deep learning for skeleton-based action recognition using temporal sliding lstm networks. In: The IEEE International Conference on Computer Vision (ICCV) (Oct 2017)
2017
Earlier work this paper cites.
Liu, M., Liu, H., Chen, C.: Enhanced skeleton visualization for view invariant human action recognition. Pattern Recognition 68
2017
Earlier work this paper cites.
Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View adaptive recurrent neural networks for high performance human action recognition from skeleton data. In: Proceedings of the IEEE international conference on computer vision. pp. 2117–2126 (2017)
2017
Earlier work this paper cites.
Li, R., Wang, S., Zhu, F., Huang, J.: Adaptive graph convolutional neural networks. In: AAAI (2018)
2018
Earlier work this paper cites.
Si, C., Jing, Y., Wang, W., Wang, L., Tan, T.: Skeleton-based action recognition with spatial reasoning and temporal stack learning. In: Proceedings of the European conference on computer vision (ECCV). pp. 103–118 (2018)
2018
Earlier work this paper cites.
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. Proceedings of the AAAI Conference on Artificial Intelligence 32
2018
Earlier work this paper cites.
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI conference on artificial intelligence. vol. 32 (2018)
2018
Cited alongside, same era.
Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., Tian, Q.: Actional-structural graph convolutional networks for skeleton-based action recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Cited alongside, same era.
Liu, J., Shahroudy, A., Perez, M., Wang, G., Duan, L.Y., Kot, A.C.: Ntu rgb+d 120: A large-scale benchmark for 3d human activity understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence 42
2019
Cited alongside, same era.
Si, C., Chen, W., Wang, W., Wang, L., Tan, T.: An attention enhanced graph convolutional lstm network for skeleton-based action recognition. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1227–1236 (2019). https://doi.org/10.1109/CVPR.2019.00132
Ye, F., Pu, S., Zhong, Q., Li, C., Xie, D., Tang, H.: Dynamic gcn: Context-enriched topology learning for skeleton-based action recognition. In: Proceedings of the 28th ACM international conference on multimedia. pp. 55–63 (2020)
2020
Later among the works it cites.
Zhang, P., Lan, C., Zeng, W., Xing, J., Xue, J., Zheng, N.: Semantics-guided neural networks for efficient skeleton-based human action recognition. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1112–1121 (2020)
2020
Later among the works it cites.
Cheng, K., Zhang, Y., He, X., Cheng, J., Lu, H.: Extremely lightweight skeleton-based action recognition with shiftgcn++. IEEE Transactions on Image Processing 30
2021
Later among the works it cites.
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
Wen, Y.H., Gao, L., Fu, H., Zhang, F.L., Xia, S.: Graph cnns with motif and variable temporal block for skeleton-based action recognition. Proceedings of the AAAI Conference on Artificial Intelligence 33
2019
Cited alongside, same era.
Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View adaptive neural networks for high performance skeleton-based human action recognition. IEEE transactions on pattern analysis and machine intelligence 41
2019
Cited alongside, same era.
Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., Lu, H.: Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Cited alongside, same era.
Cho, S., Maqbool, M., Liu, F., Foroosh, H.: Self-attention network for skeleton-based human action recognition. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 635–644 (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.: Disentangling and unifying graph convolutions for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 143–152 (2020)
2020
Cited alongside, same era.
Gu, A., Johnson, I., Goel, K., Saab, K., Dao, T., Rudra, A., Ré, C.: Combining recurrent, convolutional, and continuous-time models with linear state space layers. Advances in neural information processing systems 34
2021
Later among the works it cites.
Plizzari, C., Cannici, M., Matteucci, M.: Spatial temporal transformer network for skeleton-based action recognition. In: Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part III. pp. 694–701. Springer (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Shi, L., Zhang, Y., Cheng, J., Lu, H.: Adasgn: Adapting joint number and model size for efficient skeleton-based action recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 13413–13422 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Xu, K., Ye, F., Zhong, Q., Xie, D.: Topology-aware convolutional neural network for efficient skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 2866–2874 (2022)
2022
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Wang, Q., Shi, S., He, J., Peng, J., Liu, T., Weng, R.: Iip-transformer: Intra-inter-part transformer for skeleton-based action recognition. In: 2023 IEEE International Conference on Big Data (BigData). pp. 936–945. IEEE Computer Society, Los Alamitos, CA, USA (dec 2023). https://doi.org/10.1109/BigData59044.2023.10386970, https://doi.ieeecomputersociety.org/10.1109/BigData59044.2023.10386970
2023
Later among the works it cites.
2024
Closest in time.
Jiang, Y., Yu, S., Wang, T., Sun, Z., Wang, S.: Skeleton-based human action recognition based on single path one-shot neural architecture search. Electronics 12
2079
Closest in time.