Paul W. Fieguth

Computer Vision and Pattern Recognition · 2012–2020

A researcher in the Science corpus: 9 papers; trusts 1,345 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 614 citations into it; #6,652 of 21,575 by trust here, #4,834 by citations
• Artificial Intelligence — 306 citations into it
• Probabilistic & Bayesian Methods — 56 citations into it
• Model Architectures — 49 citations into it
• Representation & Self-Supervised Learning — 47 citations into it
• Media Technology — 46 citations into it
• Aerospace Engineering — 43 citations into it
• Computational Mechanics — 29 citations into it

Whom Paul W. Fieguth trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Andrew Zisserman
6. David G. Lowe
7. Karen Simonyan
8. Yann LeCun
9. Yoshua Bengio
10. Diederik P. Kingma

Who trusts Paul W. Fieguth overall

1. Li Liu
2. Anne Humeau-Heurtier
3. Octavio Loyola-González
4. Fan Hu
5. Shiyong Cui

Papers from Paul W. Fieguth's perspective

Ordered by how strongly the researchers Paul W. Fieguth 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

• Deep Learning for Generic Object Detection: A Survey (2018) — 2,829 citations
• Texture Classification from Random Features (2012) — 321 citations
• Extended local binary patterns for texture classification (2012) — 339 citations
• Median Robust Extended Local Binary Pattern for Texture Classification (2016) — 421 citations
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges (2020) — 2,678 citations
• From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018) — 360 citations
• Local binary features for texture classification: Taxonomy and experimental study (2017) — 353 citations
• Extended local binary patterns for face recognition (2016) — 132 citations
• A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure (2015) — 947 citations