Kai Oliver Arras

Computer Vision and Pattern Recognition · 2007–2020

A researcher in the Science corpus: 9 papers; trusts 238 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 86 citations into it; #4,814 of 21,575 by trust here, #7,092 by citations
• Artificial Intelligence — 59 citations into it; #14,173 of 35,345 by trust here, #15,542 by citations
• Automotive Engineering — 33 citations into it
• Aerospace Engineering — 20 citations into it
• Control and Systems Engineering — 20 citations into it
• Ocean Engineering — 16 citations into it; #1,226 of 3,193 by trust here, #736 by citations
• Reinforcement Learning — 16 citations into it
• Safety, Risk, Reliability and Quality — 10 citations into it

Whom Kai Oliver Arras trusts overall

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

1. Paul A. Viola
2. Robert E. Schapire
3. Morgan Quigley
4. Richard S. Sutton
5. Andrew Y. Ng
6. Andrew Barto
7. Richard Szeliski
8. Michael J. Jones
9. Navneet Dalal
10. Wolfram Burgard

Who trusts Kai Oliver Arras overall

1. Luciano Spinello
2. Michael Firman
3. Matthias Luber
4. Ziyun Cai
5. Li Liu
6. Ankit Goyal
7. Julie Dequaire
8. Shuran Song
9. Mengyang Yu
10. Paulo Vinicius Koerich Borges

Papers from Kai Oliver Arras's perspective

Ordered by how strongly the researchers Kai Oliver Arras 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• People tracking with human motion predictions from social forces (2010) — 355 citations
• People detection in RGB-D data (2011) — 416 citations
• Inverse Reinforcement Learning algorithms and features for robot navigation in crowds: An experimental comparison (2014) — 164 citations
• Using Boosted Features for the Detection of People in 2D Range Data (2007) — 433 citations
• Human motion trajectory prediction: a survey (2019) — 905 citations
• People tracking in RGB-D data with on-line boosted target models (2011) — 208 citations
• SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports (2015) — 296 citations
• For Further Reading (2020) — 1,863 citations
• Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning (2016) — 122 citations