Camille Couprie

Computer Vision and Pattern Recognition · 2011–2023

A researcher in the Science corpus: 8 papers; trusts 765 researchers and is trusted by 5,730 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 185 citations into it; #354 of 21,575 by trust here, #797 by citations
• Artificial Intelligence — 63 citations into it; #26,917 of 35,345 by trust here, #11,750 by citations
• Model Architectures — 27 citations into it
• Generative Models — 24 citations into it; #166 of 1,956 by trust here, #224 by citations
• Media Technology — 15 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Aerospace Engineering — 13 citations into it
• Natural Language Processing — 9 citations into it

Whom Camille Couprie trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. L. Eon Bottou
7. Kaiming He
8. Rob Fergus
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Camille Couprie overall

1. Liang-Chieh Chen
2. Ross B. Girshick
3. Jitendra Malik
4. Trevor Darrell
5. Sergey Levine
6. Xiaogang Wang
7. Abhinav Kumar Gupta
8. Luc Van Gool
9. Yoshua Bengio
10. Yi Yang

Papers from Camille Couprie's perspective

Ordered by how strongly the researchers Camille Couprie 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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 Hierarchical Features for Scene Labeling (2013) — 2,781 citations
• Deep multi-scale video prediction beyond mean square error (2015) — 2,004 citations
• Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers (2012) — 205 citations
• Indoor Semantic Segmentation using depth information (2013) — 512 citations
• Predicting Deeper into the Future of Semantic Segmentation (2017) — 255 citations
• Semantic Segmentation using Adversarial Networks (2016) — 756 citations
• Power Watershed: A Unifying Graph-Based Optimization Framework (2011) — 331 citations
• Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar (2023) — 333 citations