Jongchan Park

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 755 researchers and is trusted by 73 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 159 citations into it; #3,639 of 21,575 by trust here, #2,401 by citations
• Artificial Intelligence — 120 citations into it; #4,578 of 35,345 by trust here, #3,167 by citations
• Representation & Self-Supervised Learning — 44 citations into it
• Model Architectures — 38 citations into it; #365 of 2,278 by trust here, #360 by citations
• Optimization & Training — 19 citations into it
• Generative Models — 12 citations into it
• Radiology, Nuclear Medicine and Imaging — 7 citations into it; #2,048 of 3,265 by trust here, #1,332 by citations
• Ocean Engineering — 6 citations into it

Whom Jongchan Park trusts overall

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

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

Who trusts Jongchan Park overall

1. Irwan Bello
2. Junsuk Choe
3. Zhongzhan Huang
4. Sanghyun Woo
5. David W. Romero
6. Meng-Hao Guo
7. Zequn Qin
8. Qibin Hou
9. Shunping Ji
10. Leiyu Chen

Papers from Jongchan Park's perspective

Ordered by how strongly the researchers Jongchan Park 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. 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

• CBAM: Convolutional Block Attention Module (2018) — 24,594 citations
• BAM: Bottleneck Attention Module (2018) — 1,311 citations
• Reducing Domain Gap by Reducing Style Bias (2021) — 471 citations
• Reducing Domain Gap via Style-Agnostic Networks (2019) — 66 citations