Changick Kim

Computer Vision and Pattern Recognition · 2002–2019

A researcher in the Science corpus: 6 papers; trusts 759 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 182 citations into it; #8,328 of 21,575 by trust here, #7,249 by citations
• Artificial Intelligence — 60 citations into it; #18,888 of 35,345 by trust here, #17,776 by citations
• Generative Models — 31 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 6 citations into it

Whom Changick Kim trusts overall

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

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

Who trusts Changick Kim overall

1. Willie Neiswanger
2. Costas I. Cotsaces
3. Frank D. Wood
4. Lukas Hoyer
5. N. Nikolaidis
6. Lin Wang
7. Ioannis Pitas

Papers from Changick Kim's perspective

Ordered by how strongly the researchers Changick 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
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

• Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 (2017) — 70 citations
• The Sixth Visual Object Tracking VOT2018 Challenge Results (2018) — 797 citations
• 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
• Fast and automatic video object segmentation and tracking for content-based applications (2002) — 362 citations