Publisher
Springer Nature Switzerland
Reference30 articles.
1. Chen, Y., Zhao, L., Peng, X., Yuan, J., Metaxas, D.N.: Construct dynamic graphs for hand gesture recognition via spatial-temporal attention (2019). http://arxiv.org/abs/1907.08871
2. Caputo, A., Giachetti, A., Soso, S., Pintani, D., D’Eusanio, A., Pini, S., Borghi, G., Simoni, A., Vezzani, R., Cucchiara, R., Ranieri, A., Giannini, F., Lupinetti, K., Monti, M., Maghoumi, M., LaViola Jr, J.J., Le, M.-Q., Nguyen, H.-D., Tran, M.-T.: SHREC 2021: track on skeleton-based hand gesture recognition in the wild (2021). http://arxiv.org/abs/2106.10980
3. Slama, R., Rabah, W., Wannous, H.: STr-GCN: dual spatial graph convolutional network and transformer graph encoder for 3D hand gesture recognition. In: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–6. IEEE, Waikoloa Beach, HI, USA (2023). https://doi.org/10.1109/FG57933.2023.10042643
4. Qi, J., Ma, L., Cui, Z., Yu, Y.: Computer vision-based hand gesture recognition for human-robot interaction: a review. Complex Intell. Syst. (2023). https://doi.org/10.1007/s40747-023-01173-6
5. Yang, F., Sakti, S., Wu, Y., Nakamura, S.: Make skeleton-based action recognition model smaller, faster and better (2020). http://arxiv.org/abs/1907.09658