Gernot Riegler

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 740 researchers and is trusted by 1,067 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 157 citations into it; #1,309 of 21,575 by trust here, #2,191 by citations
• Aerospace Engineering — 49 citations into it; #193 of 6,421 by trust here, #441 by citations
• 3D Vision & Pose — 48 citations into it; #46 of 689 by trust here, #39 by citations
• Computational Mechanics — 39 citations into it; #514 of 10,001 by trust here, #629 by citations
• Computer Graphics and Computer-Aided Design — 36 citations into it; #168 of 1,584 by trust here, #225 by citations
• Geology — 27 citations into it
• Artificial Intelligence — 17 citations into it
• Media Technology — 16 citations into it; #1,631 of 3,621 by trust here, #2,389 by citations

Whom Gernot Riegler trusts overall

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

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

Who trusts Gernot Riegler overall

1. Andreas Geiger
2. Yiyi Liao
3. Angela Dai
4. Kai Zhang
5. Michael Niemeyer
6. Alex Yu
7. Lior Yariv
8. Maxim Tatarchenko
9. Kyle Genova
10. Songyou Peng

Papers from Gernot Riegler's perspective

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

Their papers

• OctNet: Learning Deep 3D Representations at High Resolutions (2016) — 1,618 citations
• NeRF++: Analyzing and Improving Neural Radiance Fields (2020) — 1,237 citations
• OctNetFusion: Learning Depth Fusion from Data (2017) — 211 citations
• Free View Synthesis (2020) — 388 citations
• Conditioned Regression Models for Non-blind Single Image Super-Resolution (2015) — 103 citations