Andre F. de Araújo

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 656 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 128 citations into it; #7,780 of 21,575 by trust here, #6,485 by citations
• Aerospace Engineering — 41 citations into it
• Artificial Intelligence — 28 citations into it; #26,909 of 35,345 by trust here, #14,718 by citations
• Information Retrieval & Search — 25 citations into it
• Media Technology — 14 citations into it
• Model Architectures — 13 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Andre F. de Araújo trusts overall

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

1. David G. Lowe
2. Kaiming He
3. Herv'e J'egou
4. Andrew Zisserman
5. Geoffrey E. Hinton
6. Artem Babenko
7. Ondřej Chum
8. Alex Krizhevsky
9. Josef Sivic
10. Ross B. Girshick

Who trusts Andre F. de Araújo overall

1. Bingyi Cao
2. Giorgos Tolias
3. Shiv Ram Dubey
4. Jack Sim
5. Tobias Weyand
6. Chih-Kuan Yeh
7. Filip Radenovic
8. Xunlin Zhan
9. Kohei Ozaki
10. Zi Jian Yew

Papers from Andre F. de Araújo's perspective

Ordered by how strongly the researchers Andre F. de Araújo 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. Gradient-based learning applied to document recognition
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. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Large-Scale Image Retrieval with Attentive Deep Local Features (2016) — 860 citations
• Google Landmarks Dataset v2 – A Large-Scale Benchmark for Instance-Level Recognition and Retrieval (2020) — 477 citations
• Computing Receptive Fields of Convolutional Neural Networks (2019) — 323 citations
• Unifying Deep Local and Global Features for Image Search (2020) — 396 citations