Stephan R. Richter

Computer Vision and Pattern Recognition · 2016–2024

A researcher in the Science corpus: 6 papers; trusts 1,198 researchers and is trusted by 1,427 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 272 citations into it; #1,091 of 21,575 by trust here, #2,081 by citations
• Artificial Intelligence — 59 citations into it
• Generative Models — 51 citations into it
• Aerospace Engineering — 46 citations into it
• 3D Vision & Pose — 42 citations into it
• Computational Mechanics — 41 citations into it; #3,116 of 10,001 by trust here, #3,190 by citations
• Computer Graphics and Computer-Aided Design — 41 citations into it; #521 of 1,584 by trust here, #1,084 by citations
• Evaluation & Benchmarks — 39 citations into it; #280 of 3,147 by trust here, #460 by citations

Whom Stephan R. Richter trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Ross B. Girshick
6. Yoshua Bengio
7. Yann LeCun
8. Karen Simonyan
9. Ilya Sutskever
10. Jitendra Malik

Who trusts Stephan R. Richter overall

1. Vladlen Koltun
2. Luc Van Gool
3. John Lambert
4. Alexey Dosovitskiy
5. Zhedong Zheng
6. Magnus Wrenninge
7. Lukas Liebel
8. Sergey I. Nikolenko
9. Yuhua Chen
10. Lukas Hoyer

Papers from Stephan R. Richter's perspective

Ordered by how strongly the researchers Stephan R. Richter 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Playing for Data: Ground Truth from Computer Games (2016) — 2,267 citations
• Playing for Benchmarks (2017) — 518 citations
• What Do Single-View 3D Reconstruction Networks Learn? (2019) — 502 citations
• Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers (2018) — 144 citations
• Depth Pro: Sharp Monocular Metric Depth in Less Than a Second (2024) — 514 citations
• Enhancing Photorealism Enhancement (2021) — 132 citations