Chunhui Gu, Chen Sun, David A Ross, Carl Vondrick, Caro-
line Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan,
George Toderici, Susanna Ricco, Rahul Sukthankar, et al.
Ava: A video dataset of spatio-temporally localized atomic
visual actions. InProceedings of the IEEE conference on
computer vision and pattern recognition, pages 6047–6056,
2018.
Agrim Gupta, Jiajun Wu, Jia Deng, and Li Fei-Fei. Siamese
masked autoencoders. arXiv preprint arXiv:2305.14344,
2023.
Michael U Gutmann and Aapo Hyvärinen. Noise-contrastive
estimation of unnormalized statistical models, with appli-
cations to natural image statistics.Journal of machine
learning research, 13(2), 2012.
Tengda Han, Weidi Xie, and Andrew Zisserman. Video
representation learning by dense predictive coding. In
Proceedings of the IEEE/CVF International Conference
on Computer Vision Workshops, pages 0–0, 2019.
Tengda Han, Weidi Xie, and Andrew Zisserman. Memory-
augmented dense predictive coding for video representation
learning. InEuropean conference on computer vision, pages
312–329. Springer, 2020.
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dol-
lár, and Ross Girshick. Masked autoencoders are scalable
vision learners. arXiv preprint arXiv:2111.06377, 2021.
Geoffrey E Hinton. Connectionist learning procedures. In
Machine learning, pages 555–610. Elsevier, 1989.
Tarun Kalluri, Deepak Pathak, Manmohan Chandraker, and
Du Tran. Flavr: Flow-agnostic video representations for
fast frame interpolation. InProceedings of the IEEE/CVF
Winter Conference on Applications of Computer Vision,
pages 2071–2082, 2023.
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B
Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec
Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for
neural language models.arXiv preprint arXiv:2001.08361,
2020.
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang,
Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Vi-
ola, Tim Green, Trevor Back, Paul Natsev, et al. The
kinetics human action video dataset. arXiv preprint
arXiv:1705.06950, 2017.
Christoph Kayser, Wolfgang Einhäuser, Olaf Dümmer, Peter
König, and Konrad Körding. Extracting slow subspaces
from natural videos leads to complex cells. InArtificial
Neural Networks—ICANN 2001: International Conference
Vienna, Austria, August 21–25, 2001 Proceedings 11, pages
1075–1080. Springer, 2001.
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich.
Learning representations for automatic colorization. 2016.
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich.
Colorization as a proxy task for visual understanding. 2017.
Yann LeCun. A path towards autonomous machine intelli-
gence version 0.9. 2, 2022-06-27. 2022.
Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh, and Ming-
Hsuan Yang. Unsupervised representation learning by
sorting sequences. InProceedings of the IEEE international
conference on computer vision, pages 667–676, 2017.
Kunchang Li, Yali Wang, Peng Gao, Guanglu Song, Yu Liu,
Hongsheng Li, and Yu Qiao. Uniformer: Unified trans-
former for efficient spatiotemporal representation learning.
arXiv preprint arXiv:2201.04676, 2022.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay
regularization. arXiv preprint arXiv:1711.05101, 2017.
Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac,
Makarand Tapaswi, Ivan Laptev, and Josef Sivic.
Howto100m: Learning a text-video embedding by watch-
ing hundred million narrated video clips. InProceedings
of the IEEE/CVF international conference on computer
vision, pages 2630–2640, 2019.
Mehdi Noroozi and Paolo Favaro. Unsupervised learning of
visual representations by solving jigsaw puzzles. InEuro-
pean conference on computer vision, pages 69–84. Springer,
2016.
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Represen-
tation learning with contrastive predictive coding.arXiv
preprint arXiv:1807.03748, 2018.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy
Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez,