Seong-Jin Park

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

A researcher in the Science corpus: 3 papers; trusts 733 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 119 citations into it; #12,753 of 21,575 by trust here, #11,950 by citations
• Artificial Intelligence — 44 citations into it
• Media Technology — 33 citations into it; #3,534 of 3,621 by trust here, #2,954 by citations
• Model Architectures — 27 citations into it
• Generative Models — 17 citations into it
• Aerospace Engineering — 10 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Safety, Robustness & Interpretability — 9 citations into it

Whom Seong-Jin Park trusts overall

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

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

Who trusts Seong-Jin Park overall

1. Salman Hameed Khan
2. Saeed Anwar
3. Hongyu Liu
4. Hao Chen
5. Fahad Lateef
6. Syed Waqas Zamir
7. Zhenyu Zhang
8. Nick Barnes
9. Lin-Zhuo Chen
10. Daniel Seichter

Papers from Seong-Jin Park's perspective

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

Their papers

• SRFeat: Single Image Super-Resolution with Feature Discrimination (2018) — 187 citations
• RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation (2017) — 335 citations
• Quality-Agnostic Image Recognition via Invertible Decoder (2021) — 28 citations