Kyungdon Joo

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 142 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it
• Artificial Intelligence — 9 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Kyungdon Joo trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yann LeCun
4. Jifeng Dai
5. Alex Krizhevsky
6. Ross B. Girshick
7. Yoshua Bengio
8. Rob Fergus
9. Diederik P. Kingma
10. Karen Simonyan

Papers from Kyungdon Joo's perspective

Ordered by how strongly the researchers Kyungdon Joo 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Personalized Cinemagraphs Using Semantic Understanding and Collaborative Learning (2017) — 12 citations
• Non-Local Spatial Propagation Network for Depth Completion (2020) — 424 citations