[53] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2630–2640, 2019.
[54] Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, et al. Moments in time dataset: one million videos for event understanding. IEEE transactions on pattern analysis and machine intelligence, 42(2):502-508, 2019.
[55] Mathew Monfort, SouYoung Jin, Alexander Liu, David Harwath, Rogerio Feris, James Glass, and Aude Oliva. Spoken moments: Learning joint audio-visual representations from video descriptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14871-14881, June 2021.
[56] Seong Joon Oh, Rodrigo Benenson, Mario Fritz, and Bernt Schiele. Faceless person recognition; privacy implications in social media, 2016.
[57] AJ Piergiovanni and Michael S. Ryoo. Avid dataset: Anonymized videos from diverse countries, 2020.
[58] Francesco Pittaluga, Sanjeev Koppal, and Ayan Chakrabarti. Learning privacy preserving encodings through adversarial training. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 791-799. IEEE, 2019.
[59] Aayush Prakash, Shaad Boochoon, Mark Brophy, David Acuna, Eric Cameracci, Gavriel State, Omer Shapira, and Stan Birchfield. Structured domain randomization: Bridging the reality gap by context-aware synthetic data, 2020.
[60] Zhongzheng Ren, Yong Jae Lee, and Michael S Ryoo. Learning to anonymize faces for privacy preserving action detection. In Proceedings of the European conference on computer vision (ECCV), pages 620-636, 2018.
[61] Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor. Imagenet-21k pretraining for the masses, 2021.
[62] Ahmed Mohamed Gamal Salem, Apratim Bhattacharyya, Michael Backes, Mario Fritz, and Yang Zhang. Updates-leak: Data set inference and reconstruction attacks in online learning. In 29th USENIX Security Symposium, pages 1291-1308. USENIX, 2020.
[63] Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, and Rama Chellappa. Learning from synthetic data: Addressing domain shift for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3752-3761, 2018.
[64] Mannat Singh, Quentin Duval, Kalyan Vasudev Alwala, Haoqi Fan, Vaibhav Aggarwal, Aaron Adcock, Armand Joulin, Piotr Dólar, Christoph Feichtenhofer, Ross Girshick, Rohit Girdhar, and Ishan Misra. The effectiveness of mae pre-pretraining for billion-scale pretraining, 2023.
[65] 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.
[66] Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive multiview coding, 2020.
[67] Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training, 2022.
[68] Gül Varol, Ivan Laptev, Cordelia Schmid, and Andrew Zisserman. Synthetic humans for action recognition from unseen viewpoints. International Journal of Computer Vision, 129(7):2264-2287, 2021.
[69] 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.