Maxime Oquab

Computer Vision and Pattern Recognition · Trans. Mach. Learn. Res. · 2014–2025

A researcher in the Science corpus: 9 papers; trusts 940 researchers and is trusted by 3,116 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 296 citations into it; #659 of 21,575 by trust here, #1,030 by citations
• Artificial Intelligence — 191 citations into it; #1,363 of 35,345 by trust here, #1,304 by citations
• Representation & Self-Supervised Learning — 61 citations into it; #541 of 3,145 by trust here, #305 by citations
• Evaluation & Benchmarks — 42 citations into it; #890 of 3,147 by trust here, #1,876 by citations
• Generative Models — 29 citations into it
• Aerospace Engineering — 28 citations into it
• Model Architectures — 27 citations into it
• Statistics and Probability — 24 citations into it; #10,531 of 12,932 by trust here, #10,275 by citations

Whom Maxime Oquab trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yann LeCun
6. Yoshua Bengio
7. Ilya Sutskever
8. Diederik P. Kingma
9. David G. Lowe
10. Andrew Zisserman

Who trusts Maxime Oquab overall

1. Andrea Vedaldi
2. Pedro H. O. Pinheiro
3. Yanming Guo
4. Hakan Bilen
5. Andrew Zisserman
6. Ross B. Girshick
7. Georgia Gkioxari
8. Jitendra Malik
9. Mingsheng Long
10. Luc Van Gool

Papers from Maxime Oquab's perspective

Ordered by how strongly the researchers Maxime Oquab trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks (2014) — 3,302 citations
• Is object localization for free? - Weakly-supervised learning with convolutional neural networks (2015) — 934 citations
• DINOv2: Learning Robust Visual Features without Supervision (2023) — 9,372 citations
• Weakly supervised object recognition with convolutional neural networks (2014) — 94 citations
• Revisiting Classifier Two-Sample Tests (2016) — 527 citations
• ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization (2016) — 314 citations
• Vision Transformers Need Registers (2023) — 917 citations
• Geometrical Insights for Implicit Generative Modeling (2017) — 52 citations
• DINOv3 (2025) — 1,140 citations