Richard P. Wildes

Computer Vision and Pattern Recognition · 2005–2017

A researcher in the Science corpus: 7 papers; trusts 932 researchers and is trusted by 1,724 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #970 of 21,575 by trust here, #3,732 by citations
• Artificial Intelligence — 77 citations into it; #2,764 of 35,345 by trust here, #5,720 by citations
• Model Architectures — 29 citations into it
• Media Technology — 22 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Optimization & Training — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Richard P. Wildes trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Karen Simonyan
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Andrew Zisserman
7. Ilya Sutskever
8. Yann LeCun
9. Sergey Ioffe
10. Yoshua Bengio

Who trusts Richard P. Wildes overall

1. Christoph Feichtenhofer
2. Du Tran
3. Rohit Girdhar
4. Chuang Gan
5. X. Wang
6. Kensho Hara
7. Lorenzo Torresani
8. Deva Ramanan
9. Abhinav Kumar Gupta
10. Tien-Ping Tan

Papers from Richard P. Wildes's perspective

Ordered by how strongly the researchers Richard P. Wildes 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. Gradient-based learning applied to document recognition
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Spatiotemporal Residual Networks for Video Action Recognition (2016) — 745 citations
• Spatiotemporal Multiplier Networks for Video Action Recognition (2017) — 615 citations
• Dynamic scene understanding: The role of orientation features in space and time in scene classification (2012) — 129 citations
• Temporal Residual Networks for Dynamic Scene Recognition (2017) — 84 citations
• Bags of Spacetime Energies for Dynamic Scene Recognition (2014) — 59 citations
• Spacetime Forests with Complementary Features for Dynamic Scene Recognition (2013) — 38 citations
• A machine-vision system for iris recognition (2005) — 446 citations