Jon Almazán

Computer Vision and Pattern Recognition · 2013–2019

A researcher in the Science corpus: 5 papers; trusts 821 researchers and is trusted by 430 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 185 citations into it; #2,019 of 21,575 by trust here, #3,111 by citations
• Artificial Intelligence — 62 citations into it
• Representation & Self-Supervised Learning — 32 citations into it; #1,824 of 3,145 by trust here, #2,299 by citations
• Aerospace Engineering — 29 citations into it
• Information Retrieval & Search — 24 citations into it; #272 of 1,022 by trust here, #333 by citations
• Media Technology — 23 citations into it
• Signal Processing — 13 citations into it
• Model Architectures — 8 citations into it

Whom Jon Almazán trusts overall

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

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

Who trusts Jon Almazán overall

1. Albert Gordo
2. Shiv Ram Dubey
3. Xiang Bai
4. Bingyi Cao
5. Max Jaderberg
6. Lluís Gómez
7. Ahmet Iscen
8. Jérôme Revaud
9. Ronghang Hu
10. Andre F. de Araújo

Papers from Jon Almazán's perspective

Ordered by how strongly the researchers Jon Almazán 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. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Deep Image Retrieval: Learning Global Representations for Image Search (2016) — 855 citations
• ICDAR 2013 Robust Reading Competition (2013) — 1,473 citations
• Word Spotting and Recognition with Embedded Attributes (2014) — 528 citations
• End-to-End Learning of Deep Visual Representations for Image Retrieval (2016) — 568 citations
• Learning With Average Precision: Training Image Retrieval With a Listwise Loss (2019) — 436 citations