Rim Slama

Computer Vision and Pattern Recognition · 2014–2022

A researcher in the Science corpus: 3 papers; trusts 235 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 111 citations into it; #14,261 of 21,575 by trust here, #12,689 by citations
• Generative Models — 52 citations into it
• Artificial Intelligence — 27 citations into it
• Human-Computer Interaction — 24 citations into it; #1,232 of 2,253 by trust here, #1,011 by citations
• Biomedical Engineering — 16 citations into it; #8,454 of 10,734 by trust here, #7,109 by citations
• 3D Vision & Pose — 10 citations into it; #636 of 689 by trust here, #558 by citations
• Evaluation & Benchmarks — 6 citations into it

Whom Rim Slama trusts overall

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

1. Xiaodong Yang
2. Zicheng Liu
3. Pavan K. Turaga
4. Omar Oreifej
5. Wanqing Li
6. Chih-Jen Lin
7. Geoffrey E. Hinton
8. Alex Krizhevsky
9. Diederik P. Kingma
10. Kaiming He

Who trusts Rim Slama overall

1. Jun Liu
2. Liliana Lo Presti
3. Pichao Wang
4. Fei Han
5. Boulbaba Ben Amor
6. Diogo Carbonera Luvizon
7. Qianli Ma
8. Marco La Cascia
9. Brian Reily
10. Jingyong Su

Papers from Rim Slama's perspective

Ordered by how strongly the researchers Rim Slama 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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
• Generative Adversarial Networks for Face Generation: A Survey (2022) — 277 citations
• Grassmannian Representation of Motion Depth for 3D Human Gesture and Action Recognition (2014) — 37 citations