Junseok Kwon

Computer Vision and Pattern Recognition · 2010–2019

A researcher in the Science corpus: 4 papers; trusts 516 researchers and is trusted by 192 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 74 citations into it; #2,532 of 21,575 by trust here, #6,371 by citations
• Generative Models — 37 citations into it
• Artificial Intelligence — 22 citations into it
• Computational Mechanics — 16 citations into it; #4,056 of 10,001 by trust here, #6,877 by citations
• Aerospace Engineering — 14 citations into it
• Optimization & Training — 8 citations into it
• Graph Learning — 7 citations into it
• Computer Graphics and Computer-Aided Design — 6 citations into it; #643 of 1,584 by trust here, #1,116 by citations

Whom Junseok Kwon trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Tao Zhao
4. Dorin Comaniciu
5. Yann LeCun
6. Zia Khan
7. Diederik P. Kingma
8. Yoshua Bengio
9. Kaiming He
10. Christian Szegedy

Who trusts Junseok Kwon overall

1. Yang Li
2. Ming-Hsuan Yang
3. Huchuan Lu
4. Fanyi Xiao
5. Jianke Zhu
6. Kaihua Zhang
7. Wei Zhong
8. Tianzhu Zhang
9. Yi Wu
10. Jin Gao

Papers from Junseok Kwon's perspective

Ordered by how strongly the researchers Junseok Kwon 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Visual tracking decomposition (2010) — 1,263 citations
• Tracking by Sampling Trackers (2011) — 445 citations
• 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions (2019) — 342 citations
• Sphere Generative Adversarial Network Based on Geometric Moment Matching (2019) — 33 citations