參考文獻 |
[1] Yin, Jun, et al. "A skeleton-based action recognition system for medical condition detection." 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2019.
[2] Lin, Weiyao, et al. "Human activity recognition for video surveillance." 2008 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2008.
[3] Ma, Zaosheng. "Human action recognition in smart cultural tourism based on fusion techniques of virtual reality and SOM neural network." Computational Intelligence and Neuroscience 2021 (2021).
[4] Simonyan, Karen, and Andrew Zisserman. "Two-stream convolutional networks for action recognition in videos." Advances in neural information processing systems 27 (2014).
[5] Tran, Du, et al. "Learning spatiotemporal features with 3d convolutional networks." Proceedings of the IEEE international conference on computer vision. 2015.
[6] Zhang, Pengfei, et al. "EleAtt-RNN: Adding attentiveness to neurons in recurrent neural networks." IEEE Transactions on Image Processing 29 (2019): 1061-1073.
[7] BANERJEE, Avinandan; SINGH, Pawan Kumar; SARKAR, Ram. Fuzzy integral-based CNN classifier fusion for 3D skeleton action recognition. IEEE transactions on circuits and systems for video technology, 2020, 31.6: 2206-2216.
[8] DING, Zewei, et al. Investigation of different skeleton features for cnn-based 3d action recognition. In: 2017 IEEE International conference on multimedia & expo workshops (ICMEW). IEEE, 2017. p. 617-622.
[9] DU, Yong; FU, Yun; WANG, Liang. Skeleton based action recognition with convolutional neural network. In: 2015 3rd IAPR Asian conference on pattern recognition (ACPR). IEEE, 2015. p. 579-583.
[10] DU, Yong; WANG, Wei; WANG, Liang. Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. p. 1110-1118.
[11] ZHANG, Songyang, et al. Fusing geometric features for skeleton-based action recognition using multilayer LSTM networks. IEEE Transactions on Multimedia, 2018, 20.9: 2330-2343.
[12] LI, Chuankun, et al. Skeleton-based action recognition using LSTM and CNN. In: 2017 IEEE International conference on multimedia & expo workshops (ICMEW). IEEE, 2017. p. 585-590.
[13] KIPF, Thomas N.; WELLING, Max. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[14] CHIANG, Wei-Lin, et al. Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 2019. p. 257-266.
[15] PEI, Hongbin, et al. Geom-gcn: Geometric graph convolutional networks. arXiv preprint arXiv:2002.05287, 2020.
[16] ABU-EL-HAIJA, Sami, et al. N-gcn: Multi-scale graph convolution for semi-supervised node classification. In: uncertainty in artificial intelligence. PMLR, 2020. p. 841-851.
[17] YOU, Yuning, et al. L2-gcn: Layer-wise and learned efficient training of graph convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. p. 2127-2135.
[18] Yan, Sijie, Yuanjun Xiong, and Dahua Lin. "Spatial temporal graph convolutional networks for skeleton-based action recognition." Thirty-second AAAI conference on artificial intelligence. 2018.
[19] LIANG, Shengwen, et al. Engn: A high-throughput and energy-efficient accelerator for large graph neural networks. IEEE Transactions on Computers, 2020, 70.9: 1511-1525.
[20] YAN, Mingyu, et al. Hygcn: A gcn accelerator with hybrid architecture. In: 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE, 2020. p. 15-29.
[21] GENG, Tong, et al. AWB-GCN: A graph convolutional network accelerator with runtime workload rebalancing. In: 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 2020. p. 922-936.
[22] KININGHAM, Kevin; LEVIS, Philip; RÉ, Christopher. GRIP: A graph neural network accelerator architecture. IEEE Transactions on Computers, 2022.
[23] CHEN, Ming, et al. Simple and deep graph convolutional networks. In: International conference on machine learning. PMLR, 2020. p. 1725-1735.
[24] LI, Jiajun, et al. GCNAX: A flexible and energy-efficient accelerator for graph convolutional neural networks. In: 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE, 2021. p. 775-788.
[25] Ding, Luchang, Zhize Huang, and Gengsheng Chen. "An FPGA implementation of GCN with sparse adjacency matrix." 2019 IEEE 13th international conference on ASIC (ASICON). IEEE, 2019.
[26] Pei, Songwen, et al. "STARS: Spatial Temporal Graph Convolution Network for Action Recognition System on FPGAs." 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2021.
[27] WU, Weiwei, et al. STAR: An STGCN ARchitecture for Skeleton-Based Human Action Recognition. IEEE Transactions on Circuits and Systems I: Regular Papers, 2023.
[28] Kay, Will, et al. "The kinetics human action video dataset." arXiv preprint arXiv:1705.06950 (2017).
[29] Shahroudy, Amir, et al. "Ntu rgb+ d: A large scale dataset for 3d human activity analysis." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[30] HE, Kaiming, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 770-778. |