Hossein Azizpour

Computer Vision and Pattern Recognition · 2012–2020

A researcher in the Science corpus: 8 papers; trusts 846 researchers and is trusted by 4,308 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 199 citations into it; #487 of 21,575 by trust here, #921 by citations
• Artificial Intelligence — 112 citations into it; #9,886 of 35,345 by trust here, #7,172 by citations
• Molecular Biology — 37 citations into it
• Evaluation & Benchmarks — 33 citations into it
• Materials Chemistry — 28 citations into it; #3,242 of 11,513 by trust here, #9,927 by citations
• Graph Learning — 27 citations into it
• Representation & Self-Supervised Learning — 25 citations into it; #1,577 of 3,145 by trust here, #1,052 by citations
• Safety, Robustness & Interpretability — 21 citations into it

Whom Hossein Azizpour trusts overall

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

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

Who trusts Hossein Azizpour overall

1. Yu-Xiong Wang
2. Quanshi Zhang
3. Wanli Ouyang
4. Xiaogang Wang
5. Tao Kong
6. Andrea Vedaldi
7. Bolei Zhou
8. Yi Yang
9. Jitendra Malik
10. Martial Hebert

Papers from Hossein Azizpour's perspective

Ordered by how strongly the researchers Hossein Azizpour 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. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• CNN Features Off-the-Shelf: An Astounding Baseline for Recognition (2014) — 5,100 citations
• From generic to specific deep representations for visual recognition (2014) — 433 citations
• Object Detection Using Strongly-Supervised Deformable Part Models (2012) — 229 citations
• Factors of Transferability for a Generic ConvNet Representation (2014) — 355 citations
• Multi-view Body Part Recognition with Random Forests (2013) — 96 citations
• GraphQA: protein model quality assessment using graph convolutional networks (2020) — 127 citations
• Explainability Techniques for Graph Convolutional Networks (2019) — 335 citations
• The role of artificial intelligence in achieving the Sustainable Development Goals (2019) — 2,159 citations