Youngjin Kim

Computer Vision and Pattern Recognition · with Kim · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 612 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 128 citations into it; #6,821 of 21,575 by trust here, #5,053 by citations
• Artificial Intelligence — 94 citations into it; #13,226 of 35,345 by trust here, #7,346 by citations
• Natural Language Processing — 44 citations into it
• Multimodal Learning — 42 citations into it
• Generative Models — 35 citations into it; #1,609 of 1,956 by trust here, #1,843 by citations
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it

Whom Youngjin Kim trusts overall

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

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

Who trusts Youngjin Kim overall

1. Kwot Sin Lee
2. Chen Wang
3. Dong Gong
4. Chenyou Fan
5. Jie Lei
6. Ngoc-Trung Tran
7. Zhou Zhao
8. Pin Jiang
9. Wenshan Wang
10. Junbin Xiao

Papers from Youngjin Kim's perspective

Ordered by how strongly the researchers Youngjin 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering (2017) — 700 citations
• Video Question Answering with Spatio-Temporal Reasoning (2019) — 68 citations
• Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks (2018) — 45 citations