Clinton Fookes

Computer Vision and Pattern Recognition · 2009–2020

A researcher in the Science corpus: 7 papers; trusts 328 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 108 citations into it; #10,835 of 21,575 by trust here, #9,872 by citations
• Artificial Intelligence — 83 citations into it; #17,336 of 35,345 by trust here, #13,163 by citations
• Computational Mechanics — 31 citations into it
• Model Architectures — 26 citations into it
• Radiology, Nuclear Medicine and Imaging — 24 citations into it
• Signal Processing — 20 citations into it
• Genetics — 7 citations into it
• Natural Language Processing — 7 citations into it

Whom Clinton Fookes trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yann LeCun
5. Yoshua Bengio
6. Diederik P. Kingma
7. Christopher K. I. Williams
8. Ross B. Girshick
9. Karen Simonyan
10. Ronald J. Williams

Who trusts Clinton Fookes overall

1. Vishwanath A. Sindagi
2. Victor S. Lempitsky
3. Daniel Oñoro-Rubio

Papers from Clinton Fookes's perspective

Ordered by how strongly the researchers Clinton Fookes 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Crowd Counting Using Multiple Local Features (2009) — 321 citations
• Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection (2017) — 361 citations
• Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar (2020) — 23 citations
• Deep Learning for Medical Anomaly Detection – A Survey (2020) — 426 citations
• Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms (2020) — 345 citations
• Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective (2018) — 306 citations
• Image2Mesh: A Learning Framework for Single Image 3D Reconstruction (2017) — 138 citations