O. Le Meur
Computer Vision and Pattern Recognition · 2006–2014
A researcher in the Science corpus: 4 papers; trusts 286 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 63 citations into it; #5,867 of 21,575 by trust here, #7,750 by citations
- Cognitive Neuroscience — 18 citations into it; #3,116 of 10,852 by trust here, #3,947 by citations
- Computer Graphics and Computer-Aided Design — 12 citations into it
- Computational Mechanics — 11 citations into it
- Cognitive Science & Psychology — 6 citations into it
- Human-Computer Interaction — 5 citations into it; #263 of 2,253 by trust here, #464 by citations
Whom O. Le Meur trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Laurent Itti
- Alexei A. Efros
- Kaiming He
- Antonio Torralba
- B. Tatler
- Claudio M. Privitera
- Neil D. B. Bruce
- Marcelo Bertalmío
- Derrick J. Parkhurst
- Nikos Komodakis
Who trusts O. Le Meur overall
- Tilke Judd
- Ali Borji
- Nicolas Riche
- Ruohan Zhang
- Wenguan Wang
- Zoya Bylinskii
- Frédo Durand
- Hae Jong Seo
- Akanksha Saran
- Antonio Torralba
Papers from O. Le Meur's perspective
Ordered by how strongly the researchers O. Le Meur trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- A coherent computational approach to model bottom-up visual attention (2006) — 698 citations
- Methods for comparing scanpaths and saliency maps: strengths and weaknesses (2012) — 344 citations
- Predicting visual fixations on video based on low-level visual features. (2007) — 338 citations
- Image Inpainting : Overview and Recent Advances (2014) — 604 citations