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We present the largest and most comprehensive empirical study of pre-trained visual representations (PVRs) or visual 'foundation models' for Embodied AI.
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Kunihiko Fukushima · 1975
Earlier work this paper cites.
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Earlier work this paper cites.
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Earlier work this paper cites.
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Earlier work this paper cites.
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Real robot challenge 2020
Real Robot Challenge 2020 · 2020
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Dandan Shan, Jiaqi Geng, Michelle Shu, and David F Fouhey · 2020
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Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick · 2020
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DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
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https://sites.google.com/view/robohive , 2020
Robohive – a unified framework for robot learning · 2020
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Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimir Vondrus, Sameer Dharur, Franziska Meier, Wojciech Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, and Dhruv Batra · 2021
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Learning transferable visual models from natural language supervision
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
Masked contrastive representation learning
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H Liu, L Lee, K Lee, and P Abbeel · 2022
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Scaling vision transformers
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
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Erik Wijmans, Irfan Essa, and Dhruv Batra · 2022
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Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Self-supervised pretraining of visual features in the wild
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Last layer re-training is sufficient for robustness to spurious correlations
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Surgical fine-tuning improves adaptation to distribution shifts
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, Quoc Le, and Denny Zhou · 2022
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Zero experience required: Plug & play modular transfer learning for semantic visual navigation
Ziad Al-Halah, Santhosh K Ramakrishnan, and Kristen Grauman · 2022
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Habitat challenge 2022, 2022c
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Cacti: A framework for scalable multi-task multi-scene visual imitation learning
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Ovrl-v2: A simple state-of-art baseline for imagenav and objectnav
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Language-driven representation learning for robotics
Siddharth Karamcheti, Suraj Nair, Annie S Chen, Thomas Kollar, Chelsea Finn, Dorsa Sadigh, and Percy Liang · 2023
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Liv: Language-image representations and rewards for robotic control
Yecheng Jason Ma, William Liang, Vaidehi Som, Vikash Kumar, Amy Zhang, Osbert Bastani, and Dinesh Jayaraman · 2023
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Open-world object manipulation using pre-trained vision-language models
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Pirlnav: Pretraining with imitation and rl finetuning for objectnav
Ram Ramrakhya, Dhruv Batra, Erik Wijmans, and Abhishek Das · 2023
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