Jingen Liu

Computer Vision and Pattern Recognition · with Shah · 2008–2019

A researcher in the Science corpus: 8 papers; trusts 1,097 researchers and is trusted by 1,390 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 187 citations into it; #1,095 of 21,575 by trust here, #2,287 by citations
• Artificial Intelligence — 102 citations into it; #2,516 of 35,345 by trust here, #4,656 by citations
• Representation & Self-Supervised Learning — 38 citations into it; #1,609 of 3,145 by trust here, #1,430 by citations
• Biomedical Engineering — 20 citations into it; #3,565 of 10,734 by trust here, #7,109 by citations
• Model Architectures — 13 citations into it
• Human-Computer Interaction — 10 citations into it; #663 of 2,253 by trust here, #714 by citations
• Natural Language Processing — 7 citations into it
• Control and Systems Engineering — 5 citations into it

Whom Jingen Liu trusts overall

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

1. Ivan Laptev
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. David G. Lowe
7. Ross B. Girshick
8. Pedro F. Felzenszwalb
9. Yann LeCun
10. Karen Simonyan

Who trusts Jingen Liu overall

1. Xinxiao Wu
2. Mubarak Shah
3. Yu-Gang Jiang
4. Angela Yao
5. Jingjing Zheng
6. Anan Liu
7. Zhang Zhang
8. Kishore K. Reddy
9. Hossein Rahmani
10. Dacheng Tao

Papers from Jingen Liu's perspective

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

Their papers

• Recognizing realistic actions from videos “in the wild” (2009) — 1,201 citations
• Recognizing human actions by attributes (2011) — 630 citations
• Cross-view action recognition via view knowledge transfer (2011) — 289 citations
• Learning human actions via information maximization (2008) — 486 citations
• Recognizing human actions using multiple features (2008) — 321 citations
• Customizable Architecture Search for Semantic Segmentation (2019) — 151 citations
• Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction. (2018) — 171 citations
• Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos (2015) — 27 citations