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,
Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al.
Dinov2: Learning robust visual features without supervi-
sion. arXiv preprint arXiv:2304.07193, 2023.
Nikhil Parthasarathy, SM Eslami, João Carreira, and
Olivier J Hénaff. Self-supervised video pretraining
yields strong image representations. arXiv preprint
arXiv:2210.06433, 2022.
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor
Darrell, and Alexei A Efros. Context encoders: Feature
learning by inpainting. InProceedings of the IEEE con-
ference on computer vision and pattern recognition, pages
2536–2544, 2016.
Silvia L Pintea, Jan C van Gemert, and Arnold WM Smeul-
ders. Déja vu: Motion prediction in static images. In
Computer Vision–ECCV 2014: 13th European Conference,
Zurich, Switzerland, September 6-12, 2014, Proceedings,
Part III 13, pages 172–187. Springer, 2014.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya
Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry,
Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learn-
ing transferable visual models from natural language su-
pervision. InInternational conference on machine learning,
pages 8748–8763. PMLR, 2021.
Rajesh PN Rao and Dana H Ballard. Predictive coding
in the visual cortex: a functional interpretation of some
extra-classical receptive-field effects.Nature neuroscience,
2(1):79–87, 1999.
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, San-
jeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy,
Aditya Khosla, Michael Bernstein, Alexander C. Berg, and
12