Martin Kiefel

Computer Vision and Pattern Recognition · 2012–2017

A researcher in the Science corpus: 5 papers; trusts 759 researchers and is trusted by 162 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 116 citations into it; #2,855 of 21,575 by trust here, #4,750 by citations
• Artificial Intelligence — 23 citations into it; #12,482 of 35,345 by trust here, #13,163 by citations
• Computational Mechanics — 21 citations into it; #2,608 of 10,001 by trust here, #1,190 by citations
• Evaluation & Benchmarks — 16 citations into it
• 3D Vision & Pose — 14 citations into it
• Aerospace Engineering — 14 citations into it; #5,976 of 6,421 by trust here, #3,644 by citations
• Model Architectures — 10 citations into it
• Biomedical Engineering — 9 citations into it

Whom Martin Kiefel trusts overall

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

1. Justin Domke
2. Philipp Krahenbuhl
3. Yann LeCun
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Matthew D. Zeiler
7. Kaiming He
8. Pedro F. Felzenszwalb
9. Andrew Zisserman
10. Navneet Dalal

Who trusts Martin Kiefel overall

1. Michael J. Black
2. Ijaz Akhter
3. Philipp Hennig
4. Jun Xie
5. Federica Bogo
6. Angjoo Kanazawa
7. Georgios Pavlakos
8. Andreas Geiger
9. Anurag Arnab
10. Thiemo Alldieck

Papers from Martin Kiefel's perspective

Ordered by how strongly the researchers Martin Kiefel 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. 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

• Unite the People: Closing the Loop Between 3D and 2D Human Representations (2017) — 585 citations
• Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks (2015) — 179 citations
• Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer (2015) — 172 citations
• Human Pose Estimation with Fields of Parts (2014) — 81 citations
• Quasi-Newton Methods: A New Direction (2012) — 110 citations