José Ramón Padilla-López

Computer Vision and Pattern Recognition · 2014

A researcher in the Science corpus: 3 papers; trusts 126 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 82 citations into it; #9,113 of 21,575 by trust here, #13,542 by citations
• Biomedical Engineering — 30 citations into it; #7,613 of 10,734 by trust here, #9,032 by citations
• Human-Computer Interaction — 29 citations into it; #1,937 of 2,253 by trust here, #1,440 by citations
• Artificial Intelligence — 20 citations into it; #28,815 of 35,345 by trust here, #29,412 by citations
• 3D Vision & Pose — 13 citations into it
• Control and Systems Engineering — 10 citations into it

Whom José Ramón Padilla-López trusts overall

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

1. Xiaodong Yang
2. Jiang Wang
3. Wanqing Li
4. Zicheng Liu
5. Yingli Tian
6. Ferda Ofli
7. Rizwan Ahmed Chaudhry
8. Lu Xia
9. Ioannis Kapsouras
10. Hossein Rahmani

Who trusts José Ramón Padilla-López overall

1. Fei Han
2. Enea Cippitelli
3. Liliana Lo Presti
4. Brian Reily
5. Samuele Gasparrini
6. Marco La Cascia
7. Victoria Bloom
8. William A. Hoff
9. Ennio Gambi
10. Hao Zhang

Papers from José Ramón Padilla-López's perspective

Ordered by how strongly the researchers José Ramón Padilla-López 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. ImageNet: A large-scale hierarchical image database
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset (2014) — 47 citations
• A Vision-Based System for Intelligent Monitoring: Human Behaviour Analysis and Privacy by Context (2014) — 94 citations
• Evolutionary joint selection to improve human action recognition with RGB-D devices (2014) — 153 citations