J. A. D. Santos

Computer Vision and Pattern Recognition · 2010–2016

A researcher in the Science corpus: 3 papers; trusts 481 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #3,777 of 21,575 by trust here, #4,955 by citations
• Media Technology — 20 citations into it; #245 of 3,621 by trust here, #328 by citations
• Artificial Intelligence — 17 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Model Architectures — 8 citations into it
• Aerospace Engineering — 6 citations into it
• Remote Sensing — 6 citations into it; #65 of 95 by trust here, #80 by citations

Whom J. A. D. Santos trusts overall

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

1. David G. Lowe
2. Yi Yang
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Timo Ojala
6. Andrew Zisserman
7. Karen Simonyan
8. Ross B. Girshick
9. Yoshua Bengio
10. Yann LeCun

Who trusts J. A. D. Santos overall

1. Keiller Nogueira
2. Otávio Augusto Bizetto Penatti
3. Gui-Song Xia
4. Martin Längkvist
5. Patrick Helber
6. Gong Cheng
7. Jamie Sherrah
8. Nicolas Audebert
9. Liangpei Zhang
10. Fan Hu

Papers from J. A. D. Santos's perspective

Ordered by how strongly the researchers J. A. D. Santos 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Towards better exploiting convolutional neural networks for remote sensing scene classification (2016) — 875 citations
• Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? (2015) — 705 citations
• Evaluating the Potential of Texture and Color Descriptors for Remote Sensing Image Retrieval and Classification (2010) — 89 citations