Yong-jin Liu

Computer Vision and Pattern Recognition · 2013–2021

A researcher in the Science corpus: 6 papers; trusts 278 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #10,942 of 21,575 by trust here, #9,305 by citations
• Experimental and Cognitive Psychology — 25 citations into it; #6,573 of 9,731 by trust here, #3,670 by citations
• Generative Models — 23 citations into it; #1,184 of 1,956 by trust here, #1,796 by citations
• Artificial Intelligence — 19 citations into it
• Cognitive Neuroscience — 16 citations into it; #5,254 of 10,852 by trust here, #9,185 by citations
• Social Psychology — 15 citations into it
• Information Systems — 7 citations into it
• Model Architectures — 7 citations into it

Whom Yong-jin Liu trusts overall

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

1. Chih-Jen Lin
2. Wen-Jing Yan
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Xiaobai Li
7. Leon A. Gatys
8. Kaiming He
9. Karen Simonyan
10. Tomas Pfister

Who trusts Yong-jin Liu overall

1. Weihao Xia
2. Adrian K. Davison
3. Xianye Ben
4. Sze‐Teng Liong
5. Yujiu Yang
6. Cliff Lansley
7. Yi Ren
8. Sung Woo Park
9. Jing-Hao Xue
10. Jie Gui

Papers from Yong-jin Liu's perspective

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

Their papers

• CartoonGAN: Generative Adversarial Networks for Photo Cartoonization (2018) — 402 citations
• APDrawingGAN: Generating Artistic Portrait Drawings From Face Photos With Hierarchical GANs (2019) — 176 citations
• CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation (2014) — 958 citations
• Video-Based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms (2021) — 325 citations
• A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition (2016) — 464 citations
• CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces (2013) — 398 citations