Renaud Keriven

Computer Vision and Pattern Recognition · 1997–2012

A researcher in the Science corpus: 9 papers; trusts 132 researchers and is trusted by 198 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 79 citations into it; #2,662 of 21,575 by trust here, #5,987 by citations
• Computer Graphics and Computer-Aided Design — 35 citations into it; #389 of 1,584 by trust here, #746 by citations
• Aerospace Engineering — 26 citations into it; #958 of 6,421 by trust here, #2,141 by citations
• Computational Mechanics — 22 citations into it; #5,873 of 10,001 by trust here, #3,463 by citations
• 3D Vision & Pose — 16 citations into it; #610 of 689 by trust here, #597 by citations

Whom Renaud Keriven trusts overall

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

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

Who trusts Renaud Keriven overall

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

Papers from Renaud Keriven's perspective

Ordered by how strongly the researchers Renaud Keriven 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. Gradient-based learning applied to document recognition
4. Distinctive Image Features from Scale-Invariant Keypoints
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• 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
• Segmentation by transduction (2008) — 134 citations
• High Accuracy and Visibility-Consistent Dense Multiview Stereo (2012) — 293 citations
• A common formalism for the Integral formulations of the forward EEG problem (2005) — 432 citations
• Robust and Efficient Surface Reconstruction From Range Data (2009) — 192 citations
• CURVES: Curve evolution for vessel segmentation (2001) — 366 citations
• Level Set Methods and the Stereo Problem (1997)