Chris Russell

Computer Vision and Pattern Recognition · with Agapito · 2009–2017

A researcher in the Science corpus: 7 papers; trusts 630 researchers and is trusted by 2,321 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 132 citations into it; #809 of 21,575 by trust here, #1,821 by citations
• Artificial Intelligence — 26 citations into it; #7,029 of 35,345 by trust here, #6,468 by citations
• Aerospace Engineering — 13 citations into it
• Media Technology — 13 citations into it
• Generative Models — 12 citations into it; #991 of 1,956 by trust here, #832 by citations
• Computational Mechanics — 11 citations into it
• Human-Computer Interaction — 10 citations into it
• 3D Vision & Pose — 9 citations into it

Whom Chris Russell trusts overall

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

1. Yuri Boykov
2. Jitendra Malik
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Antonio Torralba
7. Pedro F. Felzenszwalb
8. Vladimir Kolmogorov
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Chris Russell overall

1. Anurag Arnab
2. Lubor Ladicky
3. Liang-Chieh Chen
4. Sanja Fidler
5. Philip H. S. Torr
6. Raquel Urtasun
7. Byung-soo Kim
8. Jitendra Malik
9. Andrea Vedaldi
10. Pushmeet Kohli

Papers from Chris Russell's perspective

Ordered by how strongly the researchers Chris Russell 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

• Associative hierarchical CRFs for object class image segmentation (2009) — 704 citations
• Graph Cut Based Inference with Co-occurrence Statistics (2010) — 366 citations
• What, Where and How Many? Combining Object Detectors and CRFs (2010) — 369 citations
• Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image (2017) — 532 citations
• VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning (2017) — 740 citations
• Part-based modelling of compound scenes from images (2015) — 31 citations
• Video Pop-up: Monocular 3D Reconstruction of Dynamic Scenes (2014) — 123 citations