Anurag Arnab

Computer Vision and Pattern Recognition · with Torr · 2015–2018

A researcher in the Science corpus: 6 papers; trusts 2,024 researchers and is trusted by 1,540 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 273 citations into it; #1,043 of 21,575 by trust here, #4,525 by citations
• Artificial Intelligence — 147 citations into it; #2,740 of 35,345 by trust here, #6,422 by citations
• Safety, Robustness & Interpretability — 51 citations into it
• Model Architectures — 43 citations into it; #1,585 of 2,278 by trust here, #1,261 by citations
• Evaluation & Benchmarks — 18 citations into it
• Media Technology — 15 citations into it
• Aerospace Engineering — 13 citations into it
• Natural Language Processing — 12 citations into it

Whom Anurag Arnab trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Alex Krizhevsky
4. Karen Simonyan
5. Geoffrey E. Hinton
6. Bharath Hariharan
7. Yoshua Bengio
8. Andrew Zisserman
9. Yann LeCun
10. Jitendra Malik

Who trusts Anurag Arnab overall

1. Philip H. S. Torr
2. Qizhu Li
3. Kaiming He
4. Shu Liu
5. Alexander Kirillov
6. Liang-Chieh Chen
7. Bert De Brabandere
8. Ross B. Girshick
9. Yanwei Li
10. Hengshuang Zhao

Papers from Anurag Arnab's perspective

Ordered by how strongly the researchers Anurag Arnab 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

• Pixelwise Instance Segmentation with a Dynamically Instantiated Network (2017) — 242 citations
• Higher Order Conditional Random Fields in Deep Neural Networks (2015) — 232 citations
• Weakly- and Semi-Supervised Panoptic Segmentation (2018) — 169 citations
• Bottom-up Instance Segmentation using Deep Higher-Order CRFs (2016) — 54 citations
• On the Robustness of Semantic Segmentation Models to Adversarial Attacks (2017) — 339 citations
• Higher Order Potentials in End-to-End Trainable Conditional Random Fields (2015) — 23 citations