Jonas Uhrig

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 5 papers; trusts 828 researchers and is trusted by 236 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 191 citations into it; #2,513 of 21,575 by trust here, #3,426 by citations
• Artificial Intelligence — 51 citations into it
• Aerospace Engineering — 32 citations into it
• Model Architectures — 29 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Media Technology — 16 citations into it
• Computational Mechanics — 10 citations into it
• Optimization & Training — 9 citations into it

Whom Jonas Uhrig trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Yann LeCun
7. Shaoqing Ren
8. Yoshua Bengio
9. Christian Szegedy
10. Karen Simonyan

Who trusts Jonas Uhrig overall

1. Liang-Chieh Chen
2. Huiyu Wang
3. Zeeshan Hayder
4. Davy Neven
5. Mengye Ren
6. Yukun Zhu
7. George Papandreou
8. Jamie Watson
9. Anurag Arnab
10. Raquel Urtasun

Papers from Jonas Uhrig's perspective

Ordered by how strongly the researchers Jonas Uhrig 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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

• DeMoN: Depth and Motion Network for Learning Monocular Stereo (2016) — 748 citations
• Pixel-Level Encoding and Depth Layering for Instance-Level Semantic Labeling (2016) — 180 citations
• Sparsity Invariant CNNs (2017) — 967 citations
• Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications (2016) — 109 citations
• Box2Pix: Single-Shot Instance Segmentation by Assigning Pixels to Object Boxes (2018) — 68 citations