Thierry Bouwmans
Computer Vision and Pattern Recognition · 2008–2019
A researcher in the Science corpus: 9 papers; trusts 659 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 200 citations into it; #4,310 of 21,575 by trust here, #8,122 by citations
- Artificial Intelligence — 78 citations into it; #21,075 of 35,345 by trust here, #26,682 by citations
- Computational Mechanics — 78 citations into it; #5,604 of 10,001 by trust here, #4,039 by citations
- Signal Processing — 39 citations into it
- Media Technology — 36 citations into it; #830 of 3,621 by trust here, #1,598 by citations
- Biomedical Engineering — 24 citations into it
- Statistics and Probability — 22 citations into it
- Model Architectures — 20 citations into it
Whom Thierry Bouwmans trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- E.J. Candes
- Yaser Sheikh
- Robert Tibshirani
- Geoffrey E. Hinton
- Zoran Zivkovic
- Kaiming He
- Liyuan Li
- Ahmed M. Elgammal
- Olivier Barnich
- Alex Krizhevsky
Who trusts Thierry Bouwmans overall
- Andrews Cordolino Sobral
- Marc Braham
- Yi Wang
- Nauman Shahid
- Sajid Javed
- Kai-Hsiang Lin
- Pierre-Marc Jodoin
- Soon Ki Jung
- Belmar García García
- Marc Van Droogenbroeck
Papers from Thierry Bouwmans's perspective
Ordered by how strongly the researchers Thierry Bouwmans trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey (2011) — 344 citations
- Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance (2014) — 461 citations
- Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019) — 302 citations
- Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation (2018) — 340 citations
- Maximum Margin Criterion (2018)
- Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery (2017) — 315 citations
- Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015) — 346 citations
- Traditional and recent approaches in background modeling for foreground detection: An overview (2014) — 690 citations
- Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey (2008) — 564 citations