Edmond Boyer

Computer Vision and Pattern Recognition · 2006–2018

A researcher in the Science corpus: 8 papers; trusts 624 researchers and is trusted by 597 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 149 citations into it; #1,747 of 21,575 by trust here, #2,332 by citations
• Artificial Intelligence — 47 citations into it; #4,716 of 35,345 by trust here, #9,968 by citations
• Computational Mechanics — 33 citations into it; #4,212 of 10,001 by trust here, #3,763 by citations
• Biomedical Engineering — 20 citations into it; #113 of 10,734 by trust here, #896 by citations
• Graph Learning — 19 citations into it
• Model Architectures — 16 citations into it
• Human-Computer Interaction — 14 citations into it; #116 of 2,253 by trust here, #66 by citations
• Signal Processing — 11 citations into it

Whom Edmond Boyer trusts overall

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

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

Who trusts Edmond Boyer overall

1. Hossein Rahmani
2. Jingen Liu
3. Jingjing Zheng
4. Mubarak Shah
5. Xiaogang Wang
6. Daniel Weinland
7. Ajmal Saeed Mian
8. Ali Farhadi
9. Sarvesh Vishwakarma
10. Ronald Poppe

Papers from Edmond Boyer's perspective

Ordered by how strongly the researchers Edmond Boyer 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A survey of vision-based methods for action representation, segmentation and recognition (2011) — 1,099 citations
• Action Recognition from Arbitrary Views using 3D Exemplars (2007) — 519 citations
• Free viewpoint action recognition using motion history volumes (2006) — 1,002 citations
• FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis (2017) — 363 citations
• Scene Flow from Depth and Color Images (2011) — 46 citations
• Dynamic Filters in Graph Convolutional Networks (2017) — 26 citations
• Multilinear Autoencoder for 3D Face Model Learning (2018) — 49 citations
• Human Shape and Pose Tracking Using Keyframes (2014) — 34 citations