Jean-Philippe Pons

Computer Vision and Pattern Recognition · 2005–2012

A researcher in the Science corpus: 6 papers; trusts 77 researchers and is trusted by 135 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #2,958 of 21,575 by trust here, #6,954 by citations
• Computer Graphics and Computer-Aided Design — 37 citations into it; #390 of 1,584 by trust here, #746 by citations
• Aerospace Engineering — 28 citations into it; #959 of 6,421 by trust here, #2,141 by citations
• Computational Mechanics — 22 citations into it; #8,164 of 10,001 by trust here, #6,041 by citations
• 3D Vision & Pose — 17 citations into it; #609 of 689 by trust here, #597 by citations

Whom Jean-Philippe Pons trusts overall

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

1. Vladimir Kolmogorov
2. S. M. Seitz
3. Yuri Boykov
4. Stanley J. Osher
5. Paul A. Viola
6. Ye Zhang
7. Bastian Goldlücke
8. Emanuel Parzen
9. Ramin Zabih
10. David G. Lowe

Who trusts Jean-Philippe Pons overall

1. Yasutaka Furukawa
2. Amaël Delaunoy
3. Marc Pollefeys
4. S. M. Seitz
5. Christian Häne
6. Brian Curless
7. Kalin S. Kolev
8. Michal Jancosek
9. Simon Hadfield
10. Martin Habbecke

Papers from Jean-Philippe Pons's perspective

Ordered by how strongly the researchers Jean-Philippe Pons 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. Distinctive Image Features from Scale-Invariant Keypoints
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score (2007) — 331 citations
• Efficient Multi-View Reconstruction of Large-Scale Scenes using Interest Points, Delaunay Triangulation and Graph Cuts (2007) — 259 citations
• Modelling dynamic scenes by registering multi-view image sequences (2005) — 145 citations
• High Accuracy and Visibility-Consistent Dense Multiview Stereo (2012) — 293 citations
• Robust and Efficient Surface Reconstruction From Range Data (2009) — 192 citations
• Minimizing the Multi-view Stereo Reprojection Error for Triangular Surface Meshes (2008) — 80 citations