Patrick Poirson

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 424 researchers and is trusted by 122 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 73 citations into it; #3,105 of 21,575 by trust here, #3,747 by citations
• Artificial Intelligence — 20 citations into it; #14,128 of 35,345 by trust here, #6,014 by citations
• Aerospace Engineering — 17 citations into it; #5,702 of 6,421 by trust here, #2,509 by citations
• Multimodal Learning — 13 citations into it
• Natural Language Processing — 11 citations into it; #2,857 of 5,688 by trust here, #1,426 by citations
• 3D Vision & Pose — 9 citations into it
• Model Architectures — 8 citations into it
• Computer Graphics and Computer-Aided Design — 6 citations into it

Whom Patrick Poirson trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Karen Simonyan
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Yasutaka Furukawa
7. Andrew Zisserman
8. Yoshua Bengio
9. Yann LeCun
10. Ilya Sutskever

Who trusts Patrick Poirson overall

1. Zhe Gan
2. Zhengyuan Yang
3. Bo Li
4. Arsalan Mousavian
5. Henghui Ding
6. Jonah Philion
7. Linjie Li
8. Licheng Yu
9. Thomas Roddick
10. Wadim Kehl

Papers from Patrick Poirson's perspective

Ordered by how strongly the researchers Patrick Poirson 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Modeling Context in Referring Expressions (2016) — 1,767 citations
• A dataset for developing and benchmarking active vision (2017) — 209 citations
• Fast Single Shot Detection and Pose Estimation (2016) — 112 citations