Informed Patch Enhanced HyperGCN for skeleton-based action recognition
Tài liệu tham khảo
Aggarwal, 2011, Human activity analysis: A review, ACM Computing Surveys, 43, 16, 10.1145/1922649.1922653
Bai, 2018, Regularized diffusion process on bidirectional context for object retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 1213, 10.1109/TPAMI.2018.2828815
Bai, 2021, Hypergraph convolution and hypergraph attention, Pattern Recognition, 110, 10.1016/j.patcog.2020.107637
Bai, S., Zhou, Z., Wang, J., Bai, X., Jan Latecki, L., & Tian, Q. (2017). Ensemble diffusion for retrieval. In Proceedings of the IEEE international conference on computer vision (pp. 774–783).
Cai, 2021, JOLO-GCN: Mining joint-centered light-weight information for skeleton-based action recognition
Cao, 2016
Carreira, 2017, Quo vadis, action recognition? A new model and the kinetics dataset
Cheng, 2020, Decoupling GCN with DropGraph module for skeleton-based action recognition
Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., & Lu, H. (2020). Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).
Defferrard, 2016, Convolutional neural networks on graphs with fast localized spectral filtering, 3844
Du, Y., Wang, W., & Wang, L. (2015). Hierarchical recurrent neural network for skeleton based action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1110–1118).
Duan, 2021, Revisiting skeleton-based action recognition, Computer Vision and Pattern Recognition
Duhme, 2021, Fusion-GCN: Multimodal action recognition using graph convolutional networks, 265
Duvenaud, 2015, Convolutional networks on graphs for learning molecular fingerprints, 2224
Feng, 2019, Hypergraph neural networks
Gao, 2012, 3-D object retrieval and recognition with hypergraph analysis, IEEE Transactions on Image Processing, 21, 4290, 10.1109/TIP.2012.2199502
Hamilton, 2017, Inductive representation learning on large graphs, 1024
Hao, 2021, Hypergraph neural network for skeleton-based action recognition, IEEE Transactions on Image Processing, 30, 2263, 10.1109/TIP.2021.3051495
Henaff, 2015
Hu, 2018, Deep bilinear learning for RGB-d action recognition
Huang, 2009, Video object segmentation by hypergraph cut
Huang, 2010, Image retrieval via probabilistic hypergraph ranking
Jianan, 2020, Temporal graph modeling for skeleton-based action recognition, Computer Vision and Pattern Recognition
Ke, Q., Bennamoun, M., An, S., Sohel, F., & Boussaid, F. (2017). 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).
Kim, 2017, Interpretable 3d human action analysis with temporal convolutional networks, 1623
Kipf, 2018
Kipf, 2016
Li, 2021, Decoupled pose and similarity based graph neural network for video person re-identification, IEEE Signal Processing Letters
Li, 2019, Node-sensitive graph fusion via topo-correlation for image retrieval, IEEE Transactions on Circuits and Systems for Video Technology, 30, 3777, 10.1109/TCSVT.2019.2944009
Li, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2018). Independently recurrent neural network (indrnn): Building a longer and deeper rnn. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5457–5466).
Li, M., Siheng, C., Xu, C., Ya, Z., Yanfeng, W., & Qi, T. (2019). Actional-structural graph convolutional networks for skeleton-based action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3595–3603).
Li, 2017, Skeleton-based action recognition with convolutional neural networks, 597
Li, Y., Zhou, H., Yin, Y., & Gao, J. (2021). Multi-label pattern image retrieval via attention mechanism driven graph convolutional network. In Proceedings of the 29th ACM international conference on multimedia (pp. 300–308).
Liu, 2017, Enhanced skeleton visualization for view invariant human action recognition, Pattern Recognition, 68, 346, 10.1016/j.patcog.2017.02.030
Liu, 2019, Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding, IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2684, 10.1109/TPAMI.2019.2916873
Liu, 2016, Spatio-temporal lstm with trust gates for 3d human action recognition, 816
Liu, 2018, Skeleton-based human action recognition with global context-aware attention LSTM networks, IEEE Transactions on Image Processing, 27, 1586, 10.1109/TIP.2017.2785279
Liu, 2018, Recognizing human actions as the evolution of pose estimation maps
Liu, Z., Zhang, H., Chen, Z., Wang, Z., & Ouyang, W. (2020). Disentangling and unifying graph convolutions for skeleton-based action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR).
Long, 2019, Semantic graph convolutional networks for 3D human pose regression
Luvizon, 2018, 2D/3D pose estimation and action recognition using multitask deep learning
Niepert, 2016, Learning convolutional neural networks for graphs, 2014
Poppe, 2010, A survey on vision-based human action recognition, Image and Vision Computing, 28, 976, 10.1016/j.imavis.2009.11.014
Shahroudy, A., Liu, J., Ng, T.-T., & Wang, G. (2016). Ntu rgb+ d: A large scale dataset for 3d human activity analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1010–1019).
Shi, L., Zhang, Y., Cheng, J., & Lu, H. (2019a). Skeleton-based action recognition with directed graph neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7912–7921).
Shi, L., Zhang, Y., Cheng, J., & Lu, H. (2019b). Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 12026–12035).
Shuman, 2013, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Processing Magazine, 30, 83, 10.1109/MSP.2012.2235192
Si, 2019, An attention enhanced graph convolutional LSTM network for skeleton-based action recognition
Tang, 2020
Wang, X., & Gupta, A. (2018). Videos as space-time region graphs. In Proceedings of the European conference on computer vision (ECCV) (pp. 399–417).
Weinland, 2011, A survey of vision-based methods for action representation, segmentation and recognition, Computer Vision and Image Understanding, 115, 224, 10.1016/j.cviu.2010.10.002
Yan, 2018, Spatial temporal graph convolutional networks for skeleton-based action recognition
Yao, T., Pan, Y., Li, Y., & Mei, T. (2018). Exploring visual relationship for image captioning. In Proceedings of the European conference on computer vision (ECCV) (pp. 684–699).
Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., & Zheng, N. (2017). 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).
Zhang, Z., Shi, Y., Yuan, C., Li, B., Wang, P., & Hu, W., et al. (2020). Object relational graph with teacher-recommended learning for video captioning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13278–13288).
Zhou, 2019, HEMlets pose: Learning part-centric heatmap triplets for accurate 3D human pose estimation
Zolfaghari, 2017, Chained multi-stream networks exploiting pose, motion, and appearance for action classification and detection