追蹤
Maxime Oquab
Maxime Oquab
Facebook AI Research
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標題
引用次數
引用次數
年份
Learning and transferring mid-level image representations using convolutional neural networks
M Oquab, L Bottou, I Laptev, J Sivic
Proceedings of the IEEE conference on computer vision and pattern …, 2014
41372014
Dinov2: Learning robust visual features without supervision
M Oquab, T Darcet, T Moutakanni, H Vo, M Szafraniec, V Khalidov, ...
arXiv preprint arXiv:2304.07193, 2023
2363*2023
Is object localization for free?-weakly-supervised learning with convolutional neural networks
M Oquab, L Bottou, I Laptev, J Sivic
Proceedings of the IEEE conference on computer vision and pattern …, 2015
11642015
Revisiting classifier two-sample tests
D Lopez-Paz, M Oquab
arXiv preprint arXiv:1610.06545, 2016
4772016
Contextlocnet: Context-aware deep network models for weakly supervised localization
V Kantorov, M Oquab, M Cho, I Laptev
Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016
3642016
Vision transformers need registers
T Darcet, M Oquab, J Mairal, P Bojanowski
arXiv preprint arXiv:2309.16588, 2023
2062023
Low bandwidth video-chat compression using deep generative models
M Oquab, P Stock, D Haziza, T Xu, P Zhang, O Celebi, Y Hasson, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
502021
Geometrical insights for implicit generative modeling
L Bottou, M Arjovsky, D Lopez-Paz, M Oquab
Braverman Readings in Machine Learning. Key Ideas from Inception to Current …, 2018
372018
Learning about an exponential amount of conditional distributions
M Belghazi, M Oquab, D Lopez-Paz
Advances in Neural Information Processing Systems 32, 2019
322019
Can RNNs learn recursive nested subject-verb agreements?
Y Lakretz, T Desbordes, JR King, B Crabbé, M Oquab, S Dehaene
arXiv preprint arXiv:2101.02258, 2021
222021
Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations
JR King, F Charton, D Lopez-Paz, M Oquab
NeuroImage 220, 117028, 2020
202020
Geometrical insights for implicit generative modeling
L Bottou, M Arjovsky, D Lopez-Paz, M Oquab
arXiv preprint arXiv:1712.07822, 2017
152017
Dimensionality and ramping: Signatures of sentence integration in the dynamics of brains and deep language models
T Desbordes, Y Lakretz, V Chanoine, M Oquab, JM Badier, A Trébuchon, ...
Journal of Neuroscience 43 (29), 5350-5364, 2023
132023
Co-training 2L submodels for visual recognition
H Touvron, M Cord, M Oquab, P Bojanowski, J Verbeek, H Jégou
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
112023
Self-appearance-aided differential evolution for motion transfer
P Liu, R Wang, X Cao, Y Zhou, A Shah, M Oquab, C Couprie, SN Lim
arXiv e-prints, arXiv: 2110.04658, 2021
82021
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
HV Vo, V Khalidov, T Darcet, T Moutakanni, N Smetanin, M Szafraniec, ...
arXiv preprint arXiv:2405.15613, 2024
72024
Consistent population control: generate plenty of points, but with a bit of resampling
V Khalidov, M Oquab, J Rapin, O Teytaud
Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic …, 2019
42019
Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning
T Moutakanni, P Bojanowski, G Chassagnon, C Hudelot, A Joulin, ...
arXiv preprint arXiv:2405.01469, 2024
32024
Discriminating the influence of correlated factors from multivariate observations: the back-to-back regression
JR King, F Charton, D Lopez-Paz, M Oquab
bioRxiv, 2020.03. 05.976936, 2020
32020
You Don't Need Data-Augmentation in Self-Supervised Learning
T Moutakanni, M Oquab, M Szafraniec, M Vakalopoulou, P Bojanowski
arXiv preprint arXiv:2406.09294, 2024
22024
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