Mohamed Daoudi

Computer Vision and Pattern Recognition · 2013–2017

A researcher in the Science corpus: 7 papers; trusts 242 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 115 citations into it; #6,209 of 21,575 by trust here, #6,281 by citations
• Human-Computer Interaction — 52 citations into it; #521 of 2,253 by trust here, #428 by citations
• Biomedical Engineering — 34 citations into it; #3,805 of 10,734 by trust here, #4,405 by citations
• Artificial Intelligence — 30 citations into it
• 3D Vision & Pose — 24 citations into it; #638 of 689 by trust here, #558 by citations
• Endocrinology, Diabetes and Metabolism — 9 citations into it
• Geometry and Topology — 8 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Mohamed Daoudi trusts overall

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

1. Zicheng Liu
2. Xiaodong Yang
3. Wanqing Li
4. Omar Oreifej
5. Christopher K. I. Williams
6. Pavan K. Turaga
7. Lu Xia
8. Jiang Wang
9. Geoffrey E. Hinton
10. Alex Krizhevsky

Who trusts Mohamed Daoudi overall

1. Jun Liu
2. Liliana Lo Presti
3. Fei Han
4. Amir Shahroudy
5. Boulbaba Ben Amor
6. Diogo Carbonera Luvizon
7. Da-Wei Zhou
8. Pichao Wang
9. Marco La Cascia

Papers from Mohamed Daoudi's perspective

Ordered by how strongly the researchers Mohamed Daoudi 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Accurate 3D action recognition using learning on the Grassmann manifold (2015) — 200 citations
• Space-Time Pose Representation for 3D Human Action Recognition (2013) — 72 citations
• Combined shape analysis of human poses and motion units for action segmentation and recognition (2015) — 19 citations
• A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition (2017) — 41 citations
• Human-object interaction recognition by learning the distances between the object and the skeleton joints (2015) — 23 citations
• Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold (2014)
• Grassmannian Representation of Motion Depth for 3D Human Gesture and Action Recognition (2014) — 37 citations