Zhengfang Duanmu
Computer Vision and Pattern Recognition · 2016–2018
A researcher in the Science corpus: 3 papers; trusts 37 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 79 citations into it; #8,082 of 21,575 by trust here, #7,750 by citations
- Media Technology — 53 citations into it; #2,510 of 3,621 by trust here, #1,365 by citations
- Evaluation & Benchmarks — 25 citations into it; #1,716 of 3,147 by trust here, #2,040 by citations
- Artificial Intelligence — 10 citations into it
- Signal Processing — 5 citations into it
Whom Zhengfang Duanmu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- A. Bovik
- Zhou Wang
- Hamid R. Sheikh
- Anush K. Moorthy
- Eero P. Simoncelli
- Lin Zhang
- Anish Mittal
- Geoffrey E. Hinton
- David J. Heeger
- Lei Zhang
Who trusts Zhengfang Duanmu overall
- Kai Zhang
- Kede Ma
- Weixia Zhang
- Wangmeng Zuo
- Chunwei Tian
- Lei Zhang
- Weihao Xia
- Ekta Prashnani
- Tobias Plötz
- Shaolin Su
Papers from Zhengfang Duanmu's perspective
Ordered by how strongly the researchers Zhengfang Duanmu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet: A large-scale hierarchical image database
- Rectified Linear Units Improve Restricted Boltzmann Machines
- Et al
- Gradient-based learning applied to document recognition
Their papers
- End-to-End Blind Image Quality Assessment Using Deep Neural Networks (2018) — 556 citations
- Waterloo Exploration Database: New Challenges for Image Quality Assessment Models (2017) — 827 citations
- Group MAD Competition? A New Methodology to Compare Objective Image Quality Models (2016) — 106 citations