| Data-efficient image recognition with contrastive predictive coding OJ Hénaff, A Srinivas, J De Fauw, A Razavi, C Doersch, SMA Eslami, ... International Conference on Machine Learning, 4182-4192, 2020 | 1825 | 2020 |
| Perceiver io: A general architecture for structured inputs & outputs A Jaegle, S Borgeaud, JB Alayrac, C Doersch, C Ionescu, D Ding, ... arXiv preprint arXiv:2107.14795, 2021 | 879 | 2021 |
| Are we done with imagenet? L Beyer, OJ Hénaff, A Kolesnikov, X Zhai, A Oord arXiv preprint arXiv:2006.07159, 2020 | 558 | 2020 |
| Paligemma: A versatile 3b vlm for transfer L Beyer, A Steiner, AS Pinto, A Kolesnikov, X Wang, D Salz, M Neumann, ... arXiv preprint arXiv:2407.07726, 2024 | 506 | 2024 |
| Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features M Tschannen, A Gritsenko, X Wang, MF Naeem, I Alabdulmohsin, ... arXiv preprint arXiv:2502.14786, 2025 | 396 | 2025 |
| Efficient visual pretraining with contrastive detection OJ Hénaff, S Koppula, JB Alayrac, A Van den Oord, O Vinyals, J Carreira Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 204 | 2021 |
| Object discovery and representation networks OJ Hénaff, S Koppula, E Shelhamer, D Zoran, A Jaegle, A Zisserman, ... European conference on computer vision, 123-143, 2022 | 120 | 2022 |
| Divide and contrast: Self-supervised learning from uncurated data Y Tian, OJ Henaff, A Van den Oord Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 119 | 2021 |
| Perceptual straightening of natural videos OJ Hénaff, RLT Goris, EP Simoncelli Nature neuroscience 22 (6), 984-991, 2019 | 119 | 2019 |
| Primary visual cortex straightens natural video trajectories OJ Hénaff, Y Bai, JA Charlton, I Nauhaus, EP Simoncelli, RLT Goris Nature communications 12 (1), 5982, 2021 | 68 | 2021 |
| Geodesics of learned representations OJ Hénaff, EP Simoncelli International Conference on Learning Representations (ICLR) 2016, 2015 | 64 | 2015 |
| Representation of visual uncertainty through neural gain variability OJ Hénaff, ZM Boundy-Singer, K Meding, CM Ziemba, RLT Goris Nature communications 11 (1), 2513, 2020 | 63 | 2020 |
| Towards in-context scene understanding I Balazevic, D Steiner, N Parthasarathy, R Arandjelović, O Henaff Advances in Neural Information Processing Systems 36, 63758-63778, 2023 | 61 | 2023 |
| Self-supervised video pretraining yields robust and more human-aligned visual representations N Parthasarathy, SM Eslami, J Carreira, O Henaff Advances in Neural Information Processing Systems 36, 65743-65765, 2023 | 47* | 2023 |
| Memory consolidation enables long-context video understanding I Balažević, Y Shi, P Papalampidi, R Chaabouni, S Koppula, OJ Hénaff arXiv preprint arXiv:2402.05861, 2024 | 45 | 2024 |
| Data curation via joint example selection further accelerates multimodal learning T Evans, N Parthasarathy, H Merzic, O Henaff Advances in Neural Information Processing Systems 37, 141240-141260, 2024 | 35 | 2024 |
| Hip: Hierarchical perceiver J Carreira, S Koppula, D Zoran, A Recasens, C Ionescu, O Henaff, ... arXiv preprint arXiv:2202.10890, 2022 | 35 | 2022 |
| Bad students make great teachers: Active learning accelerates large-scale visual understanding T Evans, S Pathak, H Merzic, J Schwarz, R Tanno, OJ Henaff European Conference on Computer Vision, 264-280, 2024 | 27 | 2024 |
| A Practitioner's Guide to Continual Multimodal Pretraining K Roth, V Udandarao, S Dziadzio, A Prabhu, M Cherti, O Vinyals, ... arXiv preprint arXiv:2408.14471, 2024 | 24 | 2024 |
| Fantastic gains and where to find them: On the existence and prospect of general knowledge transfer between any pretrained model K Roth, L Thede, AS Koepke, O Vinyals, O Hénaff, Z Akata arXiv preprint arXiv:2310.17653, 2023 | 20 | 2023 |
João CarreiraGoogle DeepMindVerified email at google.com
