Stefan Hinterstoißer

Computer Vision and Pattern Recognition · 2011–2017

A researcher in the Science corpus: 5 papers; trusts 241 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 84 citations into it; #5,006 of 21,575 by trust here, #5,053 by citations
• Artificial Intelligence — 37 citations into it
• Aerospace Engineering — 30 citations into it; #1,828 of 6,421 by trust here, #859 by citations
• Control and Systems Engineering — 14 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Reinforcement Learning — 8 citations into it
• Model Architectures — 6 citations into it
• Computational Mechanics — 5 citations into it

Whom Stefan Hinterstoißer trusts overall

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

1. Kaiming He
2. David G. Lowe
3. Navneet Dalal
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Christian Szegedy
7. Ross B. Girshick
8. Yoshua Bengio
9. Andrew Zisserman
10. Yann LeCun

Who trusts Stefan Hinterstoißer overall

1. Sergey Zakharov
2. Vincent Lepetit
3. Martin Sundermeyer
4. Yu Xiang
5. Eric Brachmann
6. Tomas Hodan
7. Paul Wohlhart
8. Dieter Fox
9. Wadim Kehl
10. Slobodan Ilic

Papers from Stefan Hinterstoißer's perspective

Ordered by how strongly the researchers Stefan Hinterstoißer trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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

• Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes (2012) — 1,493 citations
• Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes (2011) — 681 citations
• On Pre-Trained Image Features and Synthetic Images for Deep Learning (2017) — 246 citations
• Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation (2017) — 129 citations
• Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Gradient Response Maps for Real-time Detection of Texture-less Objects