Nikos Kolotouros

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 2 papers; trusts 415 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 80 citations into it; #4,235 of 21,575 by trust here, #7,092 by citations
• Computational Mechanics — 24 citations into it; #1,987 of 10,001 by trust here, #3,763 by citations
• 3D Vision & Pose — 20 citations into it
• Human-Computer Interaction — 13 citations into it
• Artificial Intelligence — 12 citations into it
• Control and Systems Engineering — 7 citations into it
• Endocrinology, Diabetes and Metabolism — 6 citations into it
• Aerospace Engineering — 5 citations into it

Whom Nikos Kolotouros trusts overall

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

1. Kaiming He
2. Georgios Pavlakos
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ross B. Girshick
6. Sergey Ioffe
7. Diederik P. Kingma
8. Yann LeCun
9. Christian Szegedy
10. Jia Deng

Who trusts Nikos Kolotouros overall

1. Gyeongsik Moon
2. Kevin Qinghong Lin
3. Muhammed Kocabas
4. Julian Chibane
5. Georgios Pavlakos
6. Hongsuk Choi
7. Yu Rong
8. Michael J. Black
9. Kyoung Mu Lee
10. Antoni Rosinol

Papers from Nikos Kolotouros's perspective

Ordered by how strongly the researchers Nikos Kolotouros 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning to Reconstruct 3D Human Pose and Shape via Model-Fitting in the Loop (2019) — 1,173 citations
• Convolutional Mesh Regression for Single-Image Human Shape Reconstruction (2019) — 588 citations