Jan Hendrik Hosang
Computer Vision and Pattern Recognition · 2014–2021
A researcher in the Science corpus: 9 papers; trusts 1,645 researchers and is trusted by 2,888 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 361 citations into it; #704 of 21,575 by trust here, #1,803 by citations
- Artificial Intelligence — 121 citations into it; #4,942 of 35,345 by trust here, #4,923 by citations
- Aerospace Engineering — 60 citations into it
- Evaluation & Benchmarks — 28 citations into it; #1,686 of 3,147 by trust here, #1,795 by citations
- Media Technology — 22 citations into it
- Model Architectures — 21 citations into it
- Automotive Engineering — 5 citations into it
- Neurology — 5 citations into it
Whom Jan Hendrik Hosang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ross B. Girshick
- Alex Krizhevsky
- Kaiming He
- Piotr Doll'ar
- Geoffrey E. Hinton
- Pedro F. Felzenszwalb
- Karen Simonyan
- Jitendra Malik
- Andrew Zisserman
- Diederik P. Kingma
Who trusts Jan Hendrik Hosang overall
- Kaiming He
- Xiaozhi Chen
- Ross B. Girshick
- Bernt Schiele
- Rodrigo Benenson
- Xiaodan Liang
- Xiaogang Wang
- Xinlei Chen
- Jitendra Malik
- Piotr Doll'ar
Papers from Jan Hendrik Hosang's perspective
Ordered by how strongly the researchers Jan Hendrik Hosang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- What Makes for Effective Detection Proposals? (2015) — 769 citations
- Simple Does It: Weakly Supervised Instance and Semantic Segmentation (2016) — 795 citations
- How good are detection proposals, really? (2014) — 274 citations
- Learning Non-maximum Suppression (2017) — 633 citations
- Taking a deeper look at pedestrians (2015) — 352 citations
- Ten Years of Pedestrian Detection, What Have We Learned? (2014) — 724 citations
- How Far are We from Solving Pedestrian Detection? (2016) — 495 citations
- COTR: Correspondence Transformer for Matching Across Images (2021) — 326 citations
- A Convnet for Non-maximum Suppression (2015) — 78 citations