[31] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021.
[32] Rui Lu, Feng Xue, Menghan Zhou, Anlong Ming, and Yu Zhou. Occlusion-shared and feature-separated network for occlusion relationship reasoning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10343-10352, 2019.
[33] Muhammad Muzammal Naseer, Kanchana Ranasinghe, Salman H Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Intriguing properties of vision transformers. Advances in Neural Information Processing Systems, 34:23296-23308, 2021.
[34] Bowen Pan, Jiankai Sun, Wuwei Lin, Limin Wang, and Weiyao Lin. Cross-stream selective networks for action recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019.
[35] Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, Serge Belongie, Alan Yuille, Philip HS Torr, and Song Bai. Occluded video instance segmentation: A benchmark. International Journal of Computer Vision, 130(8), 2022.
[36] Sylvestre-Alvise Rebuffi, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, and Timothy A Mann. Data augmentation can improve robustness. Advances in Neural Information Processing Systems, 34:29935-29948, 2021.
[37] Sebastian Ruder. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016.
[38] Madeline C Schiappa, Naman Biyani, Shruti Vyas, Hamid Palangi, Vibhav Vineet, and Yogesh Rawat. Large-scale robustness analysis of video action recognition models. arXiv preprint arXiv:2207.01398, 2022.
[39] Khurram Soomro and Amir R Zamir. Action recognition in realistic sports videos. In Computer vision in sports, pages 181-208. Springer, 2015.
[40] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012.
[41] Austin Stone, Huayan Wang, Michael Stark, Yi Liu, D Scott Phoenix, and Dileep George. Teaching compositionality to cnns. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5058-5067, 2017.
[42] Ajeet Sunil, Manav Hiren Sheth, E Shreyas, et al. Usual and unusual human activity recognition in video using deep learning and artificial intelligence for security applications. In 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), pages 1-6. IEEE, 2021.
[43] Joseph Tighe, Marc Niethammer, and Svetlana Lazebnik. Scene parsing with object instances and occlusion ordering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3748-3755, 2014.
[44] Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. VideoMAE: Masked autoencoders are data-efficient learners for self-supervised video pre-training. In Advances in Neural Information Processing Systems, 2022.
[45] Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri. A closer look at spatiotemporal convolutions for action recognition. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 6450-6459, 2018.
[46] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[47] Jianyu Wang, Cihang Xie, Zhishuai Zhang, Jun Zhu, Lingxi Xie, and Alan Yuille. Detecting semantic parts on partially occluded objects. arXiv preprint arXiv:1707.07819, 2017.