Alexandre Boulch

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 6 papers; trusts 494 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 88 citations into it; #8,948 of 21,575 by trust here, #11,286 by citations
• Media Technology — 28 citations into it; #376 of 3,621 by trust here, #648 by citations
• Computational Mechanics — 26 citations into it; #2,929 of 10,001 by trust here, #3,763 by citations
• Aerospace Engineering — 21 citations into it
• Artificial Intelligence — 20 citations into it
• Geology — 20 citations into it; #205 of 687 by trust here, #347 by citations
• Model Architectures — 18 citations into it
• Environmental Engineering — 17 citations into it; #2,704 of 5,479 by trust here, #2,527 by citations

Whom Alexandre Boulch trusts overall

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

1. Karen Simonyan
2. Evan Shelhamer
3. Yann LeCun
4. Geoffrey E. Hinton
5. Ross B. Girshick
6. Kaiming He
7. Alex Krizhevsky
8. Andrew Zisserman
9. Yoshua Bengio
10. Christian Szegedy

Who trusts Alexandre Boulch overall

1. Bertrand Le Saux
2. Loic Landrieu
3. Yuxing Chen
4. Paul Guerrero
5. Ishan Misra
6. Joris Guerry
7. Zhenwei Shi
8. Nicolas Audebert
9. Maxim Tatarchenko
10. Rodrigo Caye Daudt

Papers from Alexandre Boulch's perspective

Ordered by how strongly the researchers Alexandre Boulch trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Fully Convolutional Siamese Networks for Change Detection (2018) — 1,708 citations
• Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks (2018) — 452 citations
• Multitask learning for large-scale semantic change detection (2018) — 409 citations
• Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks (2017) — 329 citations
• SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks (2017) — 366 citations
• Deep Learning for Robust Normal Estimation in Unstructured Point Clouds (2016) — 164 citations