Roman Shapovalov

Computer Vision and Pattern Recognition · 2010–2021

A researcher in the Science corpus: 3 papers; trusts 282 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 105 citations into it; #18,380 of 21,575 by trust here, #8,831 by citations
• Computational Mechanics — 56 citations into it; #8,527 of 10,001 by trust here, #2,309 by citations
• 3D Vision & Pose — 55 citations into it; #612 of 689 by trust here, #354 by citations
• Computer Graphics and Computer-Aided Design — 47 citations into it
• Aerospace Engineering — 13 citations into it
• Geology — 10 citations into it
• Optimization & Training — 7 citations into it

Whom Roman Shapovalov trusts overall

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

1. Vladimir Kolmogorov
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ross B. Girshick
6. Diederik P. Kingma
7. B. Taskar
8. Karen Simonyan
9. Yoshua Bengio
10. Yuri Boykov

Who trusts Roman Shapovalov overall

1. Abhishek Anand
2. Vincent Leroy
3. Hema Swetha Koppula
4. Chaozheng Wu
5. Donny Y. Chen
6. Stanislaw Szymanowicz
7. Luke Melas-Kyriazi
8. Christian Rupprecht

Papers from Roman Shapovalov's perspective

Ordered by how strongly the researchers Roman Shapovalov trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction (2021) — 744 citations
• Unsupervised Learning of 3D Object Categories from Videos in the Wild (2021) — 81 citations
• NON-ASSOCIATIVE MARKOV NETWORKS FOR 3D POINT CLOUD CLASSIFICATION (2010) — 114 citations