Mircea Cimpoi

Computer Vision and Pattern Recognition · 2013–2019

A researcher in the Science corpus: 6 papers; trusts 1,017 researchers and is trusted by 1,008 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 280 citations into it; #1,361 of 21,575 by trust here, #2,081 by citations
• Aerospace Engineering — 60 citations into it; #4,396 of 6,421 by trust here, #2,141 by citations
• Artificial Intelligence — 52 citations into it
• Media Technology — 35 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Computer Graphics and Computer-Aided Design — 13 citations into it
• Geology — 11 citations into it

Whom Mircea Cimpoi trusts overall

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

1. David G. Lowe
2. Andrew Zisserman
3. Alex Krizhevsky
4. Kaiming He
5. Geoffrey E. Hinton
6. Ross B. Girshick
7. Karen Simonyan
8. Ilya Sutskever
9. Manik Varma
10. Jitendra Malik

Who trusts Mircea Cimpoi overall

1. Tsung-Yu Lin
2. Subhransu Maji
3. Yilun Du
4. Artem Babenko
5. Shu Kong
6. Li Liu
7. Leon A. Gatys
8. Yanming Guo
9. Andrea Vedaldi
10. Anne Humeau-Heurtier

Papers from Mircea Cimpoi's perspective

Ordered by how strongly the researchers Mircea Cimpoi 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

• Describing Textures in the Wild (2013) — 3,677 citations
• Deep filter banks for texture recognition and segmentation (2015) — 671 citations
• Neighbourhood Consensus Networks (2018) — 445 citations
• Deep convolutional filter banks for texture recognition and segmentation (2014) — 55 citations
• InLoc: Indoor Visual Localization with Dense Matching and View Synthesis (2019) — 555 citations
• Deep Filter Banks for Texture Recognition, Description, and Segmentation (2015) — 367 citations