Nicolas Audebert

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 610 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 115 citations into it; #10,037 of 21,575 by trust here, #12,992 by citations
• Artificial Intelligence — 52 citations into it
• Media Technology — 47 citations into it; #1,868 of 3,621 by trust here, #1,498 by citations
• Aerospace Engineering — 23 citations into it
• Atmospheric Science — 23 citations into it; #3,357 of 4,804 by trust here, #3,967 by citations
• Model Architectures — 23 citations into it
• Geology — 21 citations into it; #414 of 687 by trust here, #407 by citations
• Environmental Engineering — 15 citations into it; #854 of 5,479 by trust here, #931 by citations

Whom Nicolas Audebert trusts overall

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

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

Who trusts Nicolas Audebert overall

1. Alexandre Boulch
2. Libo Wang
3. Bertrand Le Saux
4. Xiaohui Yuan
5. Onur Tasar
6. Danfeng Hong
7. Rodrigo Caye Daudt
8. Ava Vali
9. Xin He
10. Xianping Ma

Papers from Nicolas Audebert's perspective

Ordered by how strongly the researchers Nicolas Audebert 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

• Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019) — 595 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
• Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks (2017) — 621 citations
• Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks (2016) — 406 citations