Brais Martínez

Computer Vision and Pattern Recognition · 2010–2020

A researcher in the Science corpus: 7 papers; trusts 420 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 199 citations into it; #10,186 of 21,575 by trust here, #5,806 by citations
• Artificial Intelligence — 58 citations into it; #21,859 of 35,345 by trust here, #31,361 by citations
• Experimental and Cognitive Psychology — 38 citations into it; #3,971 of 9,731 by trust here, #7,727 by citations
• Signal Processing — 28 citations into it; #3,574 of 7,572 by trust here, #5,829 by citations
• Efficiency & Compression — 15 citations into it
• Cognitive Neuroscience — 13 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Brais Martínez trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. David G. Lowe
5. Andrew Zisserman
6. Yoshua Bengio
7. Ross B. Girshick
8. Yann LeCun
9. Diederik P. Kingma
10. Karen Simonyan

Who trusts Brais Martínez overall

1. Shan Li
2. Ruohan Gao
3. Pingchuan Ma
4. Ligang Zhang
5. Xudong Cao
6. Yu Zhang
7. Kristen Grauman
8. Weihong Deng
9. Zhanpeng Zhang
10. Stavros Petridis

Papers from Brais Martínez's perspective

Ordered by how strongly the researchers Brais Martínez 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• The Visual Object Tracking VOT2016 Challenge Results (2016) — 773 citations
• Facial point detection using boosted regression and graph models (2010) — 351 citations
• ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016 (2016) — 107 citations
• Lipreading Using Temporal Convolutional Networks (2020) — 307 citations
• Knowledge distillation via adaptive instance normalization (2020) — 29 citations
• Automatic Analysis of Facial Actions: A Survey (2019) — 333 citations
• Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection (2015) — 42 citations