Bodo Rosenhahn

Computer Vision and Pattern Recognition · 2009–2021

A researcher in the Science corpus: 12 papers; trusts 798 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 185 citations into it; #4,555 of 21,575 by trust here, #4,286 by citations
• Artificial Intelligence — 77 citations into it
• Computational Mechanics — 26 citations into it; #3,211 of 10,001 by trust here, #2,407 by citations
• Control and Systems Engineering — 17 citations into it; #2,349 of 8,257 by trust here, #759 by citations
• Evaluation & Benchmarks — 17 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• 3D Vision & Pose — 14 citations into it
• Generative Models — 14 citations into it; #1,318 of 1,956 by trust here, #1,766 by citations

Whom Bodo Rosenhahn trusts overall

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

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

Who trusts Bodo Rosenhahn overall

1. Thiemo Alldieck
2. Michael J. Black
3. Georgios Pavlakos
4. Gerard Pons-Moll
5. Dushyant Mehta
6. Angjoo Kanazawa
7. Ahmed A. A. Osman
8. Marcus A. Magnor
9. Timo von Marcard
10. Christian Theobalt

Papers from Bodo Rosenhahn's perspective

Ordered by how strongly the researchers Bodo Rosenhahn 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• A Statistical Model of Human Pose and Body Shape (2009) — 473 citations
• Motion capture using joint skeleton tracking and surface estimation (2009) — 464 citations
• Posebits for Monocular Human Pose Estimation (2014) — 85 citations
• Multilinear pose and body shape estimation of dressed subjects from image sets (2010) — 141 citations
• Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs (2017) — 369 citations
• Expanding object detector's Horizon: Incremental learning framework for object detection in videos (2015) — 49 citations
• PSyCo: Manifold Span Reduction for Super Resolution (2016) — 48 citations
• Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows (2020) — 442 citations
• Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection (2021) — 329 citations
• Spatial-Temporal Transformer for Dynamic Scene Graph Generation (2021) — 176 citations
• Recovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera (2018) — 550 citations
• Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera (2018)