Marius Cordts

Computer Vision and Pattern Recognition · 2015–2016

A researcher in the Science corpus: 3 papers; trusts 635 researchers and is trusted by 2,350 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 141 citations into it; #802 of 21,575 by trust here, #740 by citations
• Artificial Intelligence — 40 citations into it
• Aerospace Engineering — 20 citations into it
• Evaluation & Benchmarks — 18 citations into it; #415 of 3,147 by trust here, #100 by citations
• Model Architectures — 12 citations into it

Whom Marius Cordts trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Pablo Arbeláez
7. Andrew Zisserman
8. Bharath Hariharan
9. Jitendra Malik
10. Yann LeCun

Who trusts Marius Cordts overall

1. Liang-Chieh Chen
2. Luc Van Gool
3. Vladlen Koltun
4. Kaiming He
5. Chunhua Shen
6. Fisher Yu
7. Anurag Arnab
8. Trevor Darrell
9. Alexander Kirillov
10. Dengxin Dai

Papers from Marius Cordts's perspective

Ordered by how strongly the researchers Marius Cordts 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

• The Cityscapes Dataset for Semantic Urban Scene Understanding (2016) — 13,806 citations
• Pixel-Level Encoding and Depth Layering for Instance-Level Semantic Labeling (2016) — 180 citations
• The Cityscapes Dataset (2015) — 399 citations