Amin Jourabloo

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 680 researchers and is trusted by 64 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 143 citations into it; #3,483 of 21,575 by trust here, #6,031 by citations
• Signal Processing — 58 citations into it; #727 of 7,572 by trust here, #1,504 by citations
• Artificial Intelligence — 40 citations into it
• Information Systems — 12 citations into it
• Media Technology — 12 citations into it
• Model Architectures — 11 citations into it
• Safety, Robustness & Interpretability — 11 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Amin Jourabloo trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Karen Simonyan
8. David G. Lowe
9. Ross B. Girshick
10. Jukka Komulainen

Who trusts Amin Jourabloo overall

1. Zitong Yu
2. Yaojie Liu
3. Xiaoming Liu
4. Adrian Bulat
5. Matan Sela
6. Elad Richardson
7. Yunpei Jia
8. Luan Tran
9. Yue Wu
10. Leonidas Spinoulas

Papers from Amin Jourabloo's perspective

Ordered by how strongly the researchers Amin Jourabloo 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting (2016) — 317 citations
• Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision (2018) — 679 citations
• Face De-Spoofing: Anti-Spoofing via Noise Modeling (2018) — 259 citations
• Face anti-spoofing using patch and depth-based CNNs (2017) — 426 citations
• Deep Tree Learning for Zero-Shot Face Anti-Spoofing (2019) — 263 citations
• Do Convolutional Neural Networks Learn Class Hierarchy? (2017) — 232 citations