Clément Godard

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 619 researchers and is trusted by 601 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 92 citations into it; #1,752 of 21,575 by trust here, #3,459 by citations
• Media Technology — 24 citations into it; #137 of 3,621 by trust here, #189 by citations
• Aerospace Engineering — 18 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Model Architectures — 13 citations into it
• 3D Vision & Pose — 12 citations into it
• Artificial Intelligence — 11 citations into it
• Optimization & Training — 8 citations into it

Whom Clément Godard trusts overall

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

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

Who trusts Clément Godard overall

1. Jamie Watson
2. Chaoqiang Zhao
3. Sudeep Pillai
4. Xinjing Cheng
5. Nan Yang
6. Rares Ambrus
7. Rowel O. Atienza
8. Marvin Klingner
9. Vincent Casser
10. Andrea Pilzer

Papers from Clément Godard's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
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

• Unsupervised Monocular Depth Estimation with Left-Right Consistency (2016) — 3,248 citations
• Digging Into Self-Supervised Monocular Depth Estimation (2018) — 2,496 citations