Ali Sharif Razavian

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 5 papers; trusts 667 researchers and is trusted by 3,397 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 181 citations into it; #607 of 21,575 by trust here, #965 by citations
• Artificial Intelligence — 73 citations into it; #14,477 of 35,345 by trust here, #7,945 by citations
• Aerospace Engineering — 27 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Information Retrieval & Search — 19 citations into it
• Media Technology — 17 citations into it
• Model Architectures — 12 citations into it
• Biophysics — 6 citations into it

Whom Ali Sharif Razavian trusts overall

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

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

Who trusts Ali Sharif Razavian overall

1. Artem Babenko
2. Bolei Zhou
3. Yu-Xiong Wang
4. Andrea Vedaldi
5. Liang Zheng
6. Yi Yang
7. Filip Radenovic
8. Antonio Torralba
9. Lingxi Xie
10. Chunhua Shen

Papers from Ali Sharif Razavian's perspective

Ordered by how strongly the researchers Ali Sharif Razavian 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. Gradient-based learning applied to document recognition
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

• 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
• Factors of Transferability for a Generic ConvNet Representation (2014) — 355 citations
• Visual Instance Retrieval with Deep Convolutional Networks (2014) — 383 citations
• Artificial intelligence for analyzing orthopedic trauma radiographs (2017) — 421 citations