Maxime Oquab

According to our database, Maxime Oquab authored at least 27 papers between 2014 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

DINOv3.

Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seung Eun Yi, Michaël Ramamonjisoa, Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang, Timothée Darcet, Théo Moutakanni, Leonel Sentana, Claire Roberts, Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie, Julien Mairal, Hervé Jégou, Patrick Labatut, Piotr Bojanowski

CoRR, August, 2025

Cluster and Predict Latent Patches for Improved Masked Image Modeling.

Timothée Darcet, Federico Baldassarre, Maxime Oquab, Julien Mairal, Piotr Bojanowski

CoRR, February, 2025

Cluster and Predict Latents Patches for Improved Masked Image Modeling.

Timothée Darcet, Federico Baldassarre, Maxime Oquab, Julien Mairal, Piotr Bojanowski

Trans. Mach. Learn. Res., 2025

DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment.

Cijo Jose, Théo Moutakanni, Dahyun Kang, Federico Baldassarre, Timothée Darcet, Hu Xu, Daniel Li, Marc Szafraniec, Michaël Ramamonjisoa, Maxime Oquab, Oriane Siméoni, Huy V. Vo, Patrick Labatut, Piotr Bojanowski

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024

Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach.

Huy V. Vo, Vasil Khalidov, Timothée Darcet, Théo Moutakanni, Nikita Smetanin, Marc Szafraniec, Hugo Touvron, Camille Couprie, Maxime Oquab, Armand Joulin, Hervé Jégou, Patrick Labatut, Piotr Bojanowski

Trans. Mach. Learn. Res., 2024

DINOv2: Learning Robust Visual Features without Supervision.

Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski

Trans. Mach. Learn. Res., 2024

You Don't Need Data-Augmentation in Self-Supervised Learning.

Théo Moutakanni, Maxime Oquab, Marc Szafraniec, Maria Vakalopoulou, Piotr Bojanowski

CoRR, 2024

Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning.

Théo Moutakanni, Piotr Bojanowski, Guillaume Chassagnon, Céline Hudelot, Armand Joulin, Yann LeCun, Matthew J. Muckley, Maxime Oquab, Marie-Pierre Revel, Maria Vakalopoulou

CoRR, 2024

You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning.

Théo Moutakanni, Maxime Oquab, Marc Szafraniec, Maria Vakalopoulou, Piotr Bojanowski

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Vision Transformers Need Registers.

Timothée Darcet, Maxime Oquab, Julien Mairal, Piotr Bojanowski

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Co-training 2L Submodels for Visual Recognition.

Hugo Touvron, Matthieu Cord, Maxime Oquab, Piotr Bojanowski, Jakob Verbeek, Hervé Jégou

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Co-training 2^L Submodels for Visual Recognition.

Hugo Touvron, Matthieu Cord, Maxime Oquab, Piotr Bojanowski, Jakob Verbeek, Hervé Jégou

CoRR, 2022

Efficient conditioned face animation using frontally-viewed embedding.

Maxime Oquab, Daniel Haziza, Ludovic Schwartz, Tao Xu, Katayoun Zand, Rui Wang, Peirong Liu, Camille Couprie

CoRR, 2022

2021

Self-appearance-aided Differential Evolution for Motion Transfer.

Peirong Liu, Rui Wang, Xuefei Cao, Yipin Zhou, Ashish Shah, Maxime Oquab, Camille Couprie, Ser-Nam Lim

CoRR, 2021

Can RNNs learn Recursive Nested Subject-Verb Agreements?

Yair Lakretz, Theo Desbordes, Jean-Rémi King, Benoît Crabbé, Maxime Oquab, Stanislas Dehaene

CoRR, 2021

Low Bandwidth Video-Chat Compression Using Deep Generative Models.

Maxime Oquab, Pierre Stock, Daniel Haziza, Tao Xu, Peizhao Zhang, Onur Celebi, Yana Hasson, Patrick Labatut, Bobo Bose-Kolanu, Thibault Peyronel, Camille Couprie

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations.

Jean-Rémi King, François Charton, David Lopez-Paz, Maxime Oquab

NeuroImage, 2020

Low Bandwidth Video-Chat Compression using Deep Generative Models.

Maxime Oquab, Pierre Stock, Oran Gafni, Daniel Haziza, Tao Xu, Peizhao Zhang, Onur Celebi, Yana Hasson, Patrick Labatut, Bobo Bose-Kolanu, Thibault Peyronel, Camille Couprie

CoRR, 2020

2019

Learning about an exponential amount of conditional distributions.

Mohamed Ishmael Belghazi, Maxime Oquab, Yann LeCun, David Lopez-Paz

CoRR, 2019

Learning about an exponential amount of conditional distributions.

Mohamed Ishmael Belghazi, Maxime Oquab, David Lopez-Paz

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Consistent population control: generate plenty of points, but with a bit of resampling.

Vasil Khalidov, Maxime Oquab, Jérémy Rapin, Olivier Teytaud

Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2019

2018

Convolutional neural networks: towards less supervision for visual recognition. (Réseaux de neurones à convolution: vers moins de supervision pour la reconnaissance visuelle).

Maxime Oquab

PhD thesis, 2018

2017

Revisiting Classifier Two-Sample Tests.

David Lopez-Paz, Maxime Oquab

Proceedings of the 5th International Conference on Learning Representations, 2017

Geometrical Insights for Implicit Generative Modeling.

Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab

Proceedings of the Braverman Readings in Machine Learning. Key Ideas from Inception to Current State, 2017

2016

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization.

Vadim Kantorov, Maxime Oquab, Minsu Cho, Ivan Laptev

Proceedings of the Computer Vision - ECCV 2016, 2016

2015

Is object localization for free? - Weakly-supervised learning with convolutional neural networks.

Maxime Oquab, Léon Bottou, Ivan Laptev, Josef Sivic

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks.

Maxime Oquab, Léon Bottou, Ivan Laptev, Josef Sivic

Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014