Wentao Liu

Computer Vision and Pattern Recognition · with Ma · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 88 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 76 citations into it; #12,781 of 21,575 by trust here, #12,419 by citations
• Media Technology — 48 citations into it
• Artificial Intelligence — 19 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Model Architectures — 9 citations into it
• Information Systems — 6 citations into it
• Information Retrieval & Search — 5 citations into it
• Neuroscience — 5 citations into it

Whom Wentao Liu trusts overall

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

1. A. Bovik
2. Zhou Wang
3. Anush K. Moorthy
4. Hamid R. Sheikh
5. Eero P. Simoncelli
6. David J. Heeger
7. Geoffrey E. Hinton
8. Kede Ma
9. Alex Krizhevsky
10. Diederik P. Kingma

Who trusts Wentao Liu overall

1. Weixia Zhang
2. Kede Ma
3. S. Alireza Golestaneh
4. Jongyoo Kim
5. Zahra Kadkhodaie
6. Jianyi Wang

Papers from Wentao Liu's perspective

Ordered by how strongly the researchers Wentao 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. ImageNet: A large-scale hierarchical image database
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. ImageNet Large Scale Visual Recognition Challenge
8. Gradient-based learning applied to document recognition
9. Attention is All you Need (Neural Information Processing Systems)
10. Long Short-Term Memory

Their papers

• End-to-End Blind Image Quality Assessment Using Deep Neural Networks (2018) — 556 citations
• dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs (2017) — 339 citations