Ramin Zabih

Computer Vision and Pattern Recognition · 2001–2021

A researcher in the Science corpus: 9 papers; trusts 537 researchers and is trusted by 7,188 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 148 citations into it; #269 of 21,575 by trust here, #895 by citations
• Artificial Intelligence — 86 citations into it
• Efficiency & Compression — 44 citations into it
• Model Architectures — 28 citations into it; #2,191 of 2,278 by trust here, #2,215 by citations
• Optimization & Training — 25 citations into it; #1,768 of 2,601 by trust here, #2,484 by citations
• Evaluation & Benchmarks — 19 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it
• Generative Models — 10 citations into it

Whom Ramin Zabih trusts overall

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

1. Yuri Boykov
2. Vladimir Kolmogorov
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Richard Szeliski
6. Kaiming He
7. Yoshua Bengio
8. Alex Graves
9. Yann LeCun
10. Diederik P. Kingma

Who trusts Ramin Zabih overall

1. Vladimir Kolmogorov
2. Ce Liu
3. Daniel Scharstein
4. Yuri Boykov
5. Antonin Chambolle
6. Heiko Hirschmüller
7. Yasutaka Furukawa
8. Andrea Vedaldi
9. Olga Veksler
10. Andrew Zisserman

Papers from Ramin Zabih's perspective

Ordered by how strongly the researchers Ramin Zabih 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
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

• Fast approximate energy minimization via graph cuts (2001) — 8,381 citations
• What energy functions can be minimized via graph cuts? (2002) — 3,738 citations
• A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors (2008) — 1,072 citations
• Computing visual correspondence with occlusions using graph cuts (2001) — 1,375 citations
• A Comparative Study of Energy Minimization Methods for Markov Random Fields (2006) — 478 citations
• Multi-camera Scene Reconstruction via Graph Cuts (2002) — 911 citations
• AutoFlow: Learning a Better Training Set for Optical Flow (2021) — 137 citations
• Channel Selection Using Gumbel Softmax (2020) — 64 citations
• An end-to-end approach for speeding up neural network inference (2018) — 5 citations