Oleksandr Sotnychenko

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 6 papers; trusts 683 researchers and is trusted by 93 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 295 citations into it; #3,345 of 21,575 by trust here, #3,489 by citations
• Human-Computer Interaction — 89 citations into it; #1,009 of 2,253 by trust here, #335 by citations
• Artificial Intelligence — 53 citations into it
• Control and Systems Engineering — 44 citations into it
• 3D Vision & Pose — 43 citations into it
• Biomedical Engineering — 41 citations into it
• Aerospace Engineering — 22 citations into it
• Model Architectures — 19 citations into it

Whom Oleksandr Sotnychenko trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ross B. Girshick
5. Yann LeCun
6. Diederik P. Kingma
7. Mykhaylo Andriluka
8. Sam Johnson
9. Yoshua Bengio
10. Andrew Zisserman

Who trusts Oleksandr Sotnychenko overall

1. Gyeongsik Moon
2. Muhammed Kocabas
3. Xiao Sun
4. Pavlo Molchanov
5. Kyoung Mu Lee
6. Ce Zheng
7. Kevin Qinghong Lin
8. Angjoo Kanazawa
9. Bugra Tekin
10. Michael J. Black

Papers from Oleksandr Sotnychenko's perspective

Ordered by how strongly the researchers Oleksandr Sotnychenko 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. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision (2016) — 1,341 citations
• GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB (2017) — 565 citations
• VNect (2017) — 874 citations
• Real-Time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor (2017) — 333 citations
• Single-Shot Multi-person 3D Pose Estimation from Monocular RGB (2017) — 460 citations
• Monocular 3D Human Pose Estimation Using Transfer Learning and Improved CNN Supervision (2016) — 51 citations