Hyo Jin Kim

Computer Vision and Pattern Recognition · with Dunn · 2015–2017

A researcher in the Science corpus: 2 papers; trusts 598 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 74 citations into it; #10,383 of 21,575 by trust here, #15,955 by citations
• Aerospace Engineering — 31 citations into it; #2,257 of 6,421 by trust here, #4,066 by citations
• Media Technology — 13 citations into it
• Artificial Intelligence — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Information Retrieval & Search — 6 citations into it
• Model Architectures — 5 citations into it

Whom Hyo Jin Kim trusts overall

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

1. David G. Lowe
2. Andrew Zisserman
3. Alex Krizhevsky
4. Herv'e J'egou
5. Geoffrey E. Hinton
6. Josef Sivic
7. Karen Simonyan
8. Kaiming He
9. Ross B. Girshick
10. Alexei A. Efros

Who trusts Hyo Jin Kim overall

1. Eric Müller-Budack
2. Giorgos Tolias
3. Kader Pustu-Iren
4. Tiantian Wang
5. Hajime Taira
6. Ralph Ewerth
7. Tobias Weyand
8. Paul Hongsuck Seo
9. Enrique Dunn
10. Tomás Jenícek

Papers from Hyo Jin Kim's perspective

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

Their papers

• Learned Contextual Feature Reweighting for Image Geo-Localization (2017) — 247 citations
• Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD (2015) — 60 citations