Yumin Suh

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 462 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 139 citations into it; #21,248 of 21,575 by trust here, #10,905 by citations
• Artificial Intelligence — 77 citations into it
• Representation & Self-Supervised Learning — 39 citations into it
• Biomedical Engineering — 29 citations into it
• Radiology, Nuclear Medicine and Imaging — 13 citations into it
• Optimization & Training — 10 citations into it

Whom Yumin Suh trusts overall

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

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

Who trusts Yumin Suh overall

1. Kan Wang
2. Kaiyang Zhou
3. Jiaxu Miao
4. Valentin Khrulkov
5. Pengfei Fang
6. Mang Ye
7. Zhedong Zheng
8. Yulin Li
9. Guan'an Wang
10. Pengfei Wang

Papers from Yumin Suh's perspective

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

Their papers

• Part-Aligned Bilinear Representations for Person Re-identification (2018) — 537 citations
• Learning to Optimize Domain Specific Normalization for Domain Generalization (2019) — 267 citations
• On Generalizing Beyond Domains in Cross-Domain Continual Learning (2022) — 44 citations
• Cross-Domain Similarity Learning for Face Recognition in Unseen Domains (2021) — 28 citations