Diogo Carbonera Luvizon

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 710 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 128 citations into it; #8,912 of 21,575 by trust here, #10,677 by citations
• Human-Computer Interaction — 48 citations into it
• Biomedical Engineering — 33 citations into it
• Artificial Intelligence — 29 citations into it; #15,368 of 35,345 by trust here, #16,471 by citations
• 3D Vision & Pose — 20 citations into it; #408 of 689 by trust here, #529 by citations
• Model Architectures — 11 citations into it
• Aerospace Engineering — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Diogo Carbonera Luvizon trusts overall

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

1. Christian Szegedy
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yann LeCun
6. Andrew Zisserman
7. Ross B. Girshick
8. Alexander Toshev
9. L. Eon Bottou
10. Karen Simonyan

Who trusts Diogo Carbonera Luvizon overall

1. Jiefeng Li
2. Ce Zheng
3. Yucheng Chen
4. Ye Yuan
5. Jun Liu
6. Donglai Xiang
7. Dario Pavllo
8. Haoshu Fang
9. Haodong Duan
10. David Picard

Papers from Diogo Carbonera Luvizon's perspective

Ordered by how strongly the researchers Diogo Carbonera Luvizon trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• 2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning (2018) — 535 citations
• Human Pose Regression by Combining Indirect Part Detection and Contextual Information (2017) — 268 citations
• Learning features combination for human action recognition from skeleton sequences (2017) — 108 citations