Taekyung Kim

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 3 papers; trusts 742 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 113 citations into it; #11,845 of 21,575 by trust here, #11,498 by citations
• Artificial Intelligence — 51 citations into it; #18,889 of 35,345 by trust here, #17,776 by citations
• Generative Models — 29 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Optimization & Training — 5 citations into it

Whom Taekyung Kim trusts overall

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

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

Who trusts Taekyung Kim overall

1. Lukas Hoyer
2. Poojan Oza
3. Jinhong Deng
4. Chaoqi Chen
5. Zhengxia Zou
6. Lin Wang
7. Han-Kai Hsu
8. Yu-Jhe Li
9. Dengxin Dai
10. Saeid Asgari Taghanaki

Papers from Taekyung Kim's perspective

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

Their papers

• Self-Ensembling With GAN-Based Data Augmentation for Domain Adaptation in Semantic Segmentation (2019) — 266 citations
• Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection (2019) — 338 citations
• Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification (2019) — 311 citations