Spyridon Leonardos

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 281 researchers and is trusted by 117 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 92 citations into it; #3,052 of 21,575 by trust here, #5,333 by citations
• Human-Computer Interaction — 23 citations into it; #396 of 2,253 by trust here, #369 by citations
• Biomedical Engineering — 14 citations into it
• Aerospace Engineering — 13 citations into it; #4,111 of 6,421 by trust here, #3,072 by citations
• Artificial Intelligence — 13 citations into it
• Control and Systems Engineering — 7 citations into it
• Optimization & Training — 6 citations into it

Whom Spyridon Leonardos trusts overall

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

1. Deva Ramanan
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Evan Shelhamer
5. Kaiming He
6. Yann LeCun
7. Pedro F. Felzenszwalb
8. Ross B. Girshick
9. Yi Yang
10. Yoshua Bengio

Who trusts Spyridon Leonardos overall

1. Georgios Pavlakos
2. Francesc Moreno-Noguer
3. Grégory Rogez
4. Angjoo Kanazawa
5. Xingyi Zhou
6. Xiao Sun
7. Xiaowei Zhou
8. Denis Tomè
9. Ruiqi Zhao
10. Julieta Martinez

Papers from Spyridon Leonardos's perspective

Ordered by how strongly the researchers Spyridon Leonardos 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

• Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video (2015) — 441 citations
• Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach (2015) — 154 citations
• MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior (2017) — 225 citations