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Skeleton-based action recognition is widely used in varied areas, e.g., surveillance and human-machine interaction.
L. Wang, Z. Tong, B. Ji, and G. Wu, “Tdn: Temporal difference networks for efficient action recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 1895–1904
1904
Earlier work this paper cites.
CMU, “Cmu graphics lab motion capture database,” http://mocap.cs.cmu.edu , 2003
2003
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” J. Mach. Learn. Research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
H. Mobahi, R. Collobert, and J. Weston, “Deep learning from temporal coherence in video,” in Proc. Int. Conf. Mach. Learn. , 2009, pp. 737–744
2009
Earlier work this paper cites.
D. Weinland, R. Ronfard, and E. Boyer, “A survey of vision-based methods for action representation, segmentation and recognition,” Comput. Vis. Image Understand. , vol. 115, no. 2, pp. 224–241, 2011
2011
Earlier work this paper cites.
J. Wang, Z. Liu, Y. Wu, and J. Yuan, “Mining actionlet ensemble for action recognition with depth cameras,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit , 2012, pp. 1290–1297
2012
Earlier work this paper cites.
S. Ji, W. Xu, M. Yang, and K. Yu, “3d convolutional neural networks for human action recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 35, pp. 221–231, 2013
2013
Earlier work this paper cites.
E. Ohn-Bar and M. Trivedi, “Joint angles similarities and hog2 for action recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops , 2013, pp. 465–470
2013
Earlier work this paper cites.
G. Evangelidis, G. Singh, and R. Horaud, “Skeletal quads: Human action recognition using joint quadruples,” in Int. Conf. Pattern Recognit. , 2014, pp. 4513–4518
2014
Earlier work this paper cites.
R. Vemulapalli, F. Arrate, and R. Chellappa, “Human action recognition by representing 3d skeletons as points in a lie group,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2014, pp. 588–595
2014
Earlier work this paper cites.
H. Rahmani, A. Mahmood, D. Q. Huynh, and A. Mian, “Hopc: Histogram of oriented principal components of 3d pointclouds for action recognition,” in Proc. Eur. Conf. Comput. Vis. , 2014, pp. 742–757
2014
Earlier work this paper cites.
J. Wang, X. Nie, Y. Xia, Y. Wu, and S.-C. Zhu, “Cross-view action modeling, learning and recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2014, pp. 2649–2656
2014
Earlier work this paper cites.
W. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent neural network regularization,” ArXiv , 2014
2014
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” ArXiv , 2015
2015
Earlier work this paper cites.
N. Srivastava, E. Mansimov, and R. Salakhudinov, “Unsupervised learning of video representations using lstms,” in Proc. Int. Conf. Mach. Learn. , 2015, pp. 843–852
2015
Earlier work this paper cites.
Y. Du, W. Wang, and L. Wang, “Hierarchical recurrent neural network for skeleton based action recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2015, pp. 1110–1118
2015
Earlier work this paper cites.
J. Liu, A. Shahroudy, D. Xu, and G. Wang, “Spatio-temporal lstm with trust gates for 3d human action recognition,” in Proc. Eur. Conf. Comput. Vis. Springer, 2016, pp. 816–833
2016
Earlier work this paper cites.
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in Proc. Eur. Conf. Comput. Vis. Springer, 2016, pp. 649–666
2016
Earlier work this paper cites.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 2536–2544
2016
Earlier work this paper cites.
M. Noroozi and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in Proc. Eur. Conf. Comput. Vis. , 2016, pp. 69–84
2016
Earlier work this paper cites.
I. Misra, C. L. Zitnick, and M. Hebert, “Shuffle and learn: unsupervised learning using temporal order verification,” in Proc. Eur. Conf. Comput. Vis. , 2016, pp. 527–544
2016
Earlier work this paper cites.
W. Zhu, C. Lan, J. Xing, W. Zeng, Y. Li, L. Shen, and X. Xie, “Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks,” in Proc. AAAI Conf. Artif. Intell. , 2016, pp. 1673–1682
2016
Earlier work this paper cites.
A. Shahroudy, J. Liu, T.-T. Ng, and G. Wang, “Ntu rgb+ d: A large scale dataset for 3d human activity analysis,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 1010–1019
2016
Earlier work this paper cites.
Z. Luo, B. Peng, D.-A. Huang, A. Alahi, and L. Fei-Fei, “Unsupervised learning of long-term motion dynamics for videos,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 2203–2212
2017
Cited alongside, same era.
H. Wang and L. Wang, “Modeling temporal dynamics and spatial configurations of actions using two-stream recurrent neural networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 499–508
2017
Cited alongside, same era.
J. Yim, D. Joo, J. Bae, and J. Kim, “A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 4133–4141
2017
Cited alongside, same era.
C. Li, P. Wang, S. Wang, Y. Hou, and W. Li, “Skeleton-based action recognition using lstm and cnn,” in Proc. IEEE Int. Conf. Multimedia Expo. Workshop , 2017, pp. 585–590
2017
Cited alongside, same era.
C. Si, W. Chen, W. Wang, L. Wang, and T. Tan, “An attention enhanced graph convolutional lstm network for skeleton-based action recognition,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 1227–1236, 2019
2019
Later among the works it cites.
J. Liu, A. Shahroudy, M. Perez, G. Wang, L.-Y. Duan, and A. C. Kot, “Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 42, no. 10, pp. 2684–2701, 2019
2019
Later among the works it cites.
K. Su, X. Liu, and E. Shlizerman, “Predict & cluster: Unsupervised skeleton based action recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 9631–9640
2020
Later among the works it cites.
L. Lin, S. Song, W. Yang, and J. Liu, “Ms2l: Multi-task self-supervised learning for skeleton based action recognition,” in Proc. ACM Multimedia Conf. , 2020, pp. 2490–2498
2020
Later among the works it cites.
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M. Liu, H. Liu, and C. Chen, “Enhanced skeleton visualization for view invariant human action recognition,” Pattern Recognit. , vol. 68, pp. 346–362, 2017
2017
Cited alongside, same era.
C. Li, Q. Zhong, D. Xie, and S. Pu, “Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation,” ArXiv , 2018
2018
Cited alongside, same era.
S. Song, C. Lan, J. Xing, W. Zeng, and J. Liu, “Spatio-temporal attention-based lstm networks for 3d action recognition and detection,” IEEE Trans. on Image Process. , vol. 27, pp. 3459–3471, 2018
2018
Cited alongside, same era.
C. Si, Y. Jing, W. Wang, L. Wang, and T. Tan, “Skeleton-based action recognition with spatial reasoning and temporal stack learning,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 103–118
2018
Cited alongside, same era.
N. Zheng, J. Wen, R. Liu, L. Long, J. Dai, and Z. Gong, “Unsupervised representation learning with long-term dynamics for skeleton based action recognition,” in Proc. AAAI Conf. Artif. Intell. , 2018
2018
Cited alongside, same era.
J. Ng and L. Davis, “Temporal difference networks for video action recognition,” in Proc. IEEE Winter Conf. Appl. Comput. Vis. , 2018, pp. 1587–1596
2018
Cited alongside, same era.
A. van den Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,” ArXiv , 2018
2018
Cited alongside, same era.
D. Kim, D. Cho, D. Yoo, and I. S. Kweon, “Learning image representations by completing damaged jigsaw puzzles,” in Proc. IEEE Winter Conf. Appl. Comput. Vis. IEEE, 2018, pp. 793–802
2018
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in Proc. Int. Conf. Mach. Learn. , 2020, pp. 1597–1607
2020
Later among the works it cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. B. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 9726–9735
2020
Later among the works it cites.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar et al. , “Bootstrap your own latent: A new approach to self-supervised learning,” ArXiv , 2020
2020
Later among the works it cites.
L. Jing and Y. Tian, “Self-supervised visual feature learning with deep neural networks: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. , 2020
2020
Later among the works it cites.
X. Liu, J. Yin, J. Liu, P. Ding, J. Liu, and H. Liub, “Trajectorycnn: a new spatio-temporal feature learning network for human motion prediction,” IEEE Trans. Circuits Syst. Video Technol. , 2020
2020
Later among the works it cites.
S. Xu, H. Rao, X. Hu, and B. Hu, “Prototypical contrast and reverse prediction: Unsupervised skeleton based action recognition,” ArXiv , 2020
2020
Later among the works it cites.
X. Chen, H. Fan, R. Girshick, and K. He, “Improved baselines with momentum contrastive learning,” ArXiv , 2020
2020
Later among the works it cites.
J. Liu, S. Song, C. Liu, Y. Li, and Y. Hu, “A benchmark dataset and comparison study for multi-modal human action analytics,” ACM Trans. Multimedia Comput. Commun. Appl. , vol. 16, pp. 1 – 24, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
Y.-B. Cheng, X. Chen, J. Chen, P. Wei, D. Zhang, and L. Lin, “Hierarchical transformer: Unsupervised representation learning for skeleton-based human action recognition,” in Proc. IEEE Int. Conf. Multimedia Expo. IEEE, 2021, pp. 1–6
2021
Closest in time.
X. Chen and K. He, “Exploring simple siamese representation learning,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit.Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 15 750–15 758
2021
Closest in time.
X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang, “Self-supervised learning: Generative or contrastive,” IEEE Trans. Knowledge Data Engineer. , 2021
2021
Closest in time.
Z. Chen, S. Li, B. Yang, Q. Li, and H. Liu, “Multi-scale spatial temporal graph convolutional network for skeleton-based action recognition,” in Proc. AAAI Conf. Artif. Intell. , 2021, pp. 1113–1122
2021
Closest in time.
S. Yang, J. Liu, S. Lu, M. H. Er, and A. C. Kot, “Skeleton cloud colorization for unsupervised 3d action representation learning,” in Proc. IEEE Int. Conf. Comput. Vis. , 2021, pp. 13 423–13 433
2021
Closest in time.
H. Rao, S. Xu, X. Hu, J. Cheng, and B. Hu, “Augmented skeleton based contrastive action learning with momentum lstm for unsupervised action recognition,” Information Sciences , vol. 569, pp. 90–109, 2021
2021
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F. M. Thoker, H. Doughty, and C. G. Snoek, “Skeleton-contrastive 3d action representation learning,” ArXiv , 2021
2021
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L. Li, M. Wang, B. Ni, H. Wang, J. Yang, and W. Zhang, “3d human action representation learning via cross-view consistency pursuit,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 4741–4750
2021
Closest in time.
Y. Su, G. Lin, and Q. Wu, “Self-supervised 3d skeleton action representation learning with motion consistency and continuity,” in Proc. IEEE Int. Conf. Comput. Vis. , 2021, pp. 13 328–13 338
2021
Closest in time.