Y. Yoo

Computer Vision and Pattern Recognition · with Choi · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 683 researchers and is trusted by 351 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 129 citations into it; #2,211 of 21,575 by trust here, #1,691 by citations
• Artificial Intelligence — 102 citations into it; #6,215 of 35,345 by trust here, #2,443 by citations
• Meta & Continual Learning — 31 citations into it; #808 of 1,041 by trust here, #350 by citations
• Optimization & Training — 20 citations into it; #883 of 2,601 by trust here, #295 by citations
• Representation & Self-Supervised Learning — 16 citations into it
• Model Architectures — 13 citations into it
• Reinforcement Learning — 8 citations into it

Whom Y. Yoo trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Ross B. Girshick
6. Diederik P. Kingma
7. Yann LeCun
8. Karen Simonyan
9. Christian Szegedy
10. Ilya Sutskever

Who trusts Y. Yoo overall

1. Huyong Wang
2. Hyun-woo Koh
3. Gyuhak Kim
4. Zhen Wang
5. Zhiqiang Shen
6. Liyuan Wang
7. Weihao Yu
8. Ali Hassani

Papers from Y. Yoo's perspective

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

Their papers

• CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features (2019) — 6,079 citations
• Rainbow Memory: Continual Learning with a Memory of Diverse Samples (2021) — 485 citations
• Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (2017) — 509 citations
• SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning (2021) — 135 citations