Ken Chatfield
Computer Vision and Pattern Recognition · 2011–2014
A researcher in the Science corpus: 3 papers; trusts 548 researchers and is trusted by 5,804 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 74 citations into it; #349 of 21,575 by trust here, #1,253 by citations
- Artificial Intelligence — 26 citations into it; #1,142 of 35,345 by trust here, #2,144 by citations
- Aerospace Engineering — 9 citations into it
- Evaluation & Benchmarks — 9 citations into it; #761 of 3,147 by trust here, #1,138 by citations
- Media Technology — 9 citations into it; #516 of 3,621 by trust here, #432 by citations
- Representation & Self-Supervised Learning — 6 citations into it; #896 of 3,145 by trust here, #1,044 by citations
- Statistical Learning Theory — 5 citations into it
Whom Ken Chatfield trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andrew Zisserman
- Andrea Vedaldi
- David G. Lowe
- Herv'e J'egou
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Karen Simonyan
- Cordelia Schmid
- Florent Perronnin
Who trusts Ken Chatfield overall
- Andrew Zisserman
- Andrea Vedaldi
- Joon Son Chung
- Kaiming He
- Yi Yang
- Karen Simonyan
- Abhinav Kumar Gupta
- Luc Van Gool
- Chunyuan Li
- Cordelia Schmid
Papers from Ken Chatfield's perspective
Ordered by how strongly the researchers Ken Chatfield trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Return of the Devil in the Details: Delving Deep into Convolutional Nets (2014) — 3,496 citations
- The devil is in the details: an evaluation of recent feature encoding methods (2011) — 953 citations
- Efficient On-the-fly Category Retrieval Using ConvNets and GPUs (2014) — 27 citations