Bugra Tekin

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 991 researchers and is trusted by 207 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 291 citations into it; #2,634 of 21,575 by trust here, #4,286 by citations
• Human-Computer Interaction — 86 citations into it; #965 of 2,253 by trust here, #450 by citations
• Control and Systems Engineering — 65 citations into it; #2,535 of 8,257 by trust here, #1,943 by citations
• Artificial Intelligence — 59 citations into it
• 3D Vision & Pose — 41 citations into it; #391 of 689 by trust here, #440 by citations
• Aerospace Engineering — 39 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Biomedical Engineering — 22 citations into it; #7,998 of 10,734 by trust here, #5,712 by citations

Whom Bugra Tekin trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. David G. Lowe
5. Alex Krizhevsky
6. Ross B. Girshick
7. Andrew Zisserman
8. Yoshua Bengio
9. Karen Simonyan
10. Yann LeCun

Who trusts Bugra Tekin overall

1. Georgios Pavlakos
2. Dushyant Mehta
3. Muhammed Kocabas
4. Yucheng Chen
5. Helge Rhodin
6. Xiaowei Zhou
7. Grégory Rogez
8. Yu Xiang
9. Dario Pavllo
10. Francesc Moreno-Noguer

Papers from Bugra Tekin's perspective

Ordered by how strongly the researchers Bugra Tekin 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Structured Prediction of 3D Human Pose with Deep Neural Networks (2016) — 280 citations
• Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation (2016) — 260 citations
• Real-Time Seamless Single Shot 6D Object Pose Prediction (2017) — 894 citations
• H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions (2019) — 287 citations
• Leveraging Photometric Consistency Over Time for Sparsely Supervised Hand-Object Reconstruction (2020) — 226 citations
• H2O: Two Hands Manipulating Objects for First Person Interaction Recognition (2021) — 299 citations
• Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation (2016) — 36 citations