Shaohui Liu

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 628 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 167 citations into it; #12,601 of 21,575 by trust here, #4,955 by citations
• Generative Models — 52 citations into it; #1,264 of 1,956 by trust here, #1,875 by citations
• Computational Mechanics — 47 citations into it
• Artificial Intelligence — 45 citations into it; #34,313 of 35,345 by trust here, #10,667 by citations
• Evaluation & Benchmarks — 20 citations into it; #1,966 of 3,147 by trust here, #3,004 by citations
• Model Architectures — 14 citations into it
• Media Technology — 11 citations into it
• Optimization & Training — 11 citations into it

Whom Shaohui Liu trusts overall

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

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

Who trusts Shaohui Liu overall

1. Ali Borji
2. Garrett Wilson
3. Songyou Peng
4. Lingjie Liu
5. Diane Joyce Cook
6. Lior Yariv
7. Eric Chan
8. Michael Niemeyer
9. Matthew Tancik
10. Shuaifeng Zhi

Papers from Shaohui Liu's perspective

Ordered by how strongly the researchers Shaohui Liu 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• RepPoints: Point Set Representation for Object Detection (2019) — 1,092 citations
• DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing (2019) — 310 citations
• Image Inpainting With Learnable Bidirectional Attention Maps (2019) — 265 citations
• An Improved Evaluation Framework for Generative Adversarial Networks (2018) — 51 citations
• Image Compressed Sensing Using Convolutional Neural Network (2020) — 396 citations