[16] Dezhao Luo, Chang Liu, Yu Zhou, Dongbao Yang, Can Ma, Qixiang Ye, and Weiping Wang. Video cloze procedure for self-supervised spatio-temporal learning. In AAAI, 2020. 1
[17] Dahun Kim, Donghyeon Cho, and In So Kweon. Self-supervised video representation learning with space-time cubic puzzles. In AAAI, 2019. 1
[18] Fei Wu, Qingzhong Wang, Jiang Bian, Ning Ding, Feixiang Lu, Jun Cheng, Dejing Dou, and Haoyi Xiong. A survey on video action recognition in sports: Datasets, methods and applications. IEE Transactions on Multimedia, 2022. 1
[19] Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Yu-Gang Jiang, Luowei Zhou, and Lu Yuan. Bevt: Bert pretraining of video transformers. arXiv preprint arXiv:2112.01529, 2021. 1, 2
[20] Beliz Gunel, Jingfei Du, Alexis Conneau, and Veselin Stoyanov. Supervised contrastive learning for pre-trained language model fine-tuning. In ICLR, 2021. 2
[21] Hyundong Cho, Chinnadhurai Sankar, Christopher Lin, Kaushik Sadagopan, Shahin Shayan-deh, Asli Celikyilmaz, Jonathan May, and Ahmad Beirami. Know thy strengths: Comprehensive dialogue state tracking diagnostics. In Findings of the ACL: EMNLP 2022, pages 5345-5359, 2022. 2
[22] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In ICML, pages 1597-1607, 2020. 2, 3, 22
[23] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In ICCV, pages 9640-9649, 2021. 2, 3
[24] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Pires, Zhaohan Guo, Mohammad Azar, et al. Bootstrap your own latent: A new approach to self-supervised learning. In NeurIPS, 2020. 2, 3, 21
[25] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In CVPR, pages 15750–15758, 2021. 2, 3, 21
[26] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In ICCV, pages 9650-9660, 2021. 2, 3, 21
[27] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dólár, and Ross Girshick. Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377, 2021. 2, 3, 21
[28] Philippe Weinzaepfel and Grégory Rogez. Mimetics: Towards understanding human actions out of context. IJCV, 129(5), 2021. 2, 3, 4, 25
[29] Jihoon Chung, Yu Wu, and Olga Russakovsky. Enabling detailed action recognition evaluation through video dataset augmentation. NeurIPS, 35:39020-39033, 2022. 2
[30] Jinpeng Wang, Yuting Gao, Ke Li, Yiqi Lin, Andy J Ma, Hao Cheng, Pai Peng, Feiyue Huang, Rongrong Ji, and Xing Sun. Removing the background by adding the background: Towards background robust self-supervised video representation learning. In CVPR, pages 11804-11813, 2021. 2
[31] Jinwoo Choi, Chen Gao, Joseph CE Messou, and Jia-Bin Huang. Why can't i dance in the mall? learning to mitigate scene bias in action recognition. NeurIPS, 32, 2019. 2
[32] Manlin Zhang, Jinpeng Wang, and Andy J Ma. Suppressing static visual cues via normalizing flows for self-supervised video representation learning. In AAAI, volume 36, pages 3300-3308, 2022. 2
[33] Yong Jae Lee, Joydeep Ghosh, and Kristen Grauman. Discovering important people and objects for egocentric video summarization. In CVPR, pages 1346-1353, 2012. 2
[34] Sheila J. Nayar. Columbia object image library (coil100). 1996. 2, 5, 6, 28