Babak Saleh

Computer Vision and Pattern Recognition · 2013–2016

A researcher in the Science corpus: 4 papers; trusts 727 researchers and is trusted by 314 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 106 citations into it; #2,265 of 21,575 by trust here, #6,146 by citations
• Artificial Intelligence — 64 citations into it; #6,875 of 35,345 by trust here, #10,065 by citations
• Representation & Self-Supervised Learning — 21 citations into it; #1,019 of 3,145 by trust here, #1,507 by citations
• Natural Language Processing — 18 citations into it; #1,733 of 5,688 by trust here, #2,440 by citations
• Evaluation & Benchmarks — 12 citations into it
• Multimodal Learning — 12 citations into it
• Media Technology — 9 citations into it
• Model Architectures — 5 citations into it

Whom Babak Saleh trusts overall

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

1. Alex Krizhevsky
2. David G. Lowe
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Karen Simonyan
7. Ilya Sutskever
8. Antonio Torralba
9. Kaiming He
10. Jia Deng

Who trusts Babak Saleh overall

1. Arun Mallya
2. Pedro Morgado
3. Hyundong Jin
4. Svetlana Lazebnik
5. Massimiliano Mancini
6. Mohamed Elhoseiny
7. Yingcong Li
8. Mengqi Xue
9. Kaiyang Zhou
10. Yunhui Guo

Papers from Babak Saleh's perspective

Ordered by how strongly the researchers Babak Saleh 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. 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

• Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions (2013) — 323 citations
• Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature (2015) — 382 citations
• Write a Classifier: Predicting Visual Classifiers from Unstructured Text (2015) — 42 citations
• The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks (2016) — 16 citations