Kede Ma
Computer Vision and Pattern Recognition · 2013–2023
A researcher in the Science corpus: 16 papers; trusts 746 researchers and is trusted by 62 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 376 citations into it; #3,444 of 21,575 by trust here, #3,558 by citations
- Media Technology — 189 citations into it; #420 of 3,621 by trust here, #373 by citations
- Artificial Intelligence — 97 citations into it
- Evaluation & Benchmarks — 84 citations into it; #1,853 of 3,147 by trust here, #2,009 by citations
- Model Architectures — 32 citations into it
- Optimization & Training — 25 citations into it
- Cognitive Neuroscience — 19 citations into it
- Representation & Self-Supervised Learning — 15 citations into it
Whom Kede Ma 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
- Diederik P. Kingma
- Lin Zhang
- Geoffrey E. Hinton
- Alex Krizhevsky
- Anish Mittal
Who trusts Kede Ma overall
- Weixia Zhang
- Kai Zhang
- Hui Li
- Guangtao Zhai
- S. Alireza Golestaneh
- Wangmeng Zuo
- Zhou Wang
- Lei Zhang
- Jianrui Cai
- Wenjing Wang
Papers from Kede Ma's perspective
Ordered by how strongly the researchers Kede Ma 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)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Image Quality Assessment: Unifying Structure and Texture Similarity (2020) — 1,428 citations
- End-to-End Blind Image Quality Assessment Using Deep Neural Networks (2018) — 556 citations
- Perceptual Quality Assessment of Smartphone Photography (2020) — 470 citations
- Waterloo Exploration Database: New Challenges for Image Quality Assessment Models (2017) — 827 citations
- Perceptual Quality Assessment for Multi-Exposure Image Fusion (2015) — 1,117 citations
- Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network (2019) — 937 citations
- Group MAD Competition? A New Methodology to Compare Objective Image Quality Models (2016) — 106 citations
- A Patch-Structure Representation Method for Quality Assessment of Contrast Changed Images (2015) — 377 citations
- I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively (2020) — 21 citations
- Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective (2023) — 409 citations
- Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild (2020) — 368 citations
- dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs (2017) — 339 citations
- Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach (2017) — 323 citations
- No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics (2015) — 359 citations
- Reversibility improved data hiding in encrypted images (2014) — 410 citations
- Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption (2013) — 835 citations