Markus Enzweiler
Computer Vision and Pattern Recognition · 2009–2016
A researcher in the Science corpus: 8 papers; trusts 644 researchers and is trusted by 3,158 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 199 citations into it; #649 of 21,575 by trust here, #668 by citations
- Artificial Intelligence — 64 citations into it; #11,451 of 35,345 by trust here, #22,463 by citations
- Aerospace Engineering — 23 citations into it; #3,713 of 6,421 by trust here, #1,425 by citations
- Evaluation & Benchmarks — 19 citations into it; #413 of 3,147 by trust here, #100 by citations
- Automotive Engineering — 13 citations into it; #62 of 3,081 by trust here, #623 by citations
- Media Technology — 13 citations into it
- Model Architectures — 9 citations into it; #887 of 2,278 by trust here, #1,826 by citations
- Natural Language Processing — 6 citations into it
Whom Markus Enzweiler trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Navneet Dalal
- David G. Lowe
- Dariu M. Gavrila
- Vladimir Naumovich Vapnik
- Ross B. Girshick
- Andrew Zisserman
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Paul A. Viola
Who trusts Markus Enzweiler overall
- Wanli Ouyang
- Piotr Doll'ar
- Alejandro González
- David Vázquez
- Bernt Schiele
- Xiaogang Wang
- Ronald Poppe
- Rodrigo Benenson
- Luc Van Gool
- Liang-Chieh Chen
Papers from Markus Enzweiler's perspective
Ordered by how strongly the researchers Markus Enzweiler 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)
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- The Cityscapes Dataset for Semantic Urban Scene Understanding (2016) — 13,806 citations
- Monocular Pedestrian Detection: Survey and Experiments (2009) — 1,350 citations
- The Cityscapes Dataset (2015) — 399 citations
- Efficient Multi-cue Scene Segmentation (2013) — 95 citations
- A Multilevel Mixture-of-Experts Framework for Pedestrian Classification (2011) — 170 citations
- Stixmantics: A Medium-Level Model for Real-Time Semantic Scene Understanding (2014) — 65 citations
- Making Bertha See (2013) — 155 citations
- Making Bertha Drive—An Autonomous Journey on a Historic Route (2014) — 842 citations