Pierre-Marc Jodoin
Computer Vision and Pattern Recognition · 2010–2019
A researcher in the Science corpus: 11 papers; trusts 404 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 164 citations into it; #7,146 of 21,575 by trust here, #3,035 by citations
- Artificial Intelligence — 71 citations into it; #16,016 of 35,345 by trust here, #21,878 by citations
- Model Architectures — 22 citations into it
- Biomedical Engineering — 19 citations into it; #3,176 of 10,734 by trust here, #4,866 by citations
- Efficiency & Compression — 18 citations into it; #1,011 of 1,264 by trust here, #1,242 by citations
- Media Technology — 17 citations into it; #3,152 of 3,621 by trust here, #1,423 by citations
- Optimization & Training — 14 citations into it
- Computational Mechanics — 10 citations into it
Whom Pierre-Marc Jodoin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Yoshua Bengio
- Ilya Sutskever
- Thierry Bouwmans
- Zoran Zivkovic
- Karen Simonyan
Who trusts Pierre-Marc Jodoin overall
- Thierry Bouwmans
- Pierre-Luc St-Charles
- Sajid Javed
- Yi Wang
- Mohammadreza Babaee
- Belmar García García
- Chris H. Bahnsen
- Guillaume-Alexandre Bilodeau
- Yang He
- Matej Kristan
Papers from Pierre-Marc Jodoin's perspective
Ordered by how strongly the researchers Pierre-Marc Jodoin 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
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- 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
- Changedetection.net: A new change detection benchmark dataset (2012) — 858 citations
- Brain tumor segmentation with Deep Neural Networks (2015) — 3,119 citations
- Non-local Deep Features for Salient Object Detection (2017) — 557 citations
- Structured Pruning of Neural Networks With Budget-Aware Regularization (2018) — 99 citations
- CDnet 2014: An Expanded Change Detection Benchmark Dataset (2014) — 864 citations
- Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography (2019) — 792 citations
- Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (2018) — 2,285 citations
- Interactive deep learning method for segmenting moving objects (2017) — 317 citations
- Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography (2016) — 104 citations
- ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI (2016) — 525 citations
- Comparative study of background subtraction algorithms (2010) — 362 citations