Jongyoo Kim
Computer Vision and Pattern Recognition · 2017–2019
A researcher in the Science corpus: 3 papers; trusts 483 researchers and is trusted by 27 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 73 citations into it; #5,348 of 21,575 by trust here, #12,689 by citations
- Media Technology — 44 citations into it; #603 of 3,621 by trust here, #1,550 by citations
- Evaluation & Benchmarks — 20 citations into it
- Model Architectures — 13 citations into it
- Artificial Intelligence — 11 citations into it
- Optimization & Training — 8 citations into it; #1,439 of 2,601 by trust here, #2,215 by citations
- Representation & Self-Supervised Learning — 6 citations into it
Whom Jongyoo Kim trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- A. Bovik
- Hamid R. Sheikh
- Diederik P. Kingma
- Zhou Wang
- Alex Krizhevsky
- Geoffrey E. Hinton
- Lin Zhang
- Anush K. Moorthy
- Yoshua Bengio
- Karen Simonyan
Who trusts Jongyoo Kim overall
- Hancheng Zhu
- Shaolin Su
- Sanghoon Lee
- S. Alireza Golestaneh
- Kede Ma
- Anh-Duc Nguyen
- Ekta Prashnani
- Weixia Zhang
- Richard Zhang
- Sebastian Bosse
Papers from Jongyoo Kim's perspective
Ordered by how strongly the researchers Jongyoo Kim trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework (2017) — 260 citations
- Fully Deep Blind Image Quality Predictor (2017) — 454 citations
- Deep CNN-Based Blind Image Quality Predictor (2019) — 306 citations