Kan Jin

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 1 paper; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 17 citations into it
• Model Architectures — 6 citations into it

Whom Kan Jin trusts overall

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

1. Karen Simonyan
2. Kaiming He
3. Christian Szegedy
4. Alex Krizhevsky
5. Andrew Zisserman
6. Geoffrey E. Hinton
7. Sergey Ioffe
8. Yann LeCun
9. Ting Zhao
10. Gao Huang

Papers from Kan Jin's perspective

Ordered by how strongly the researchers Kan Jin 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. Gradient-based learning applied to document recognition
4. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Attention mechanism-based CNN for facial expression recognition (2020) — 312 citations