948 citations · 3k across the 36 of their papers we have counts for
7 papers · 2 filters
Image Quality Assessment in the Modern Age
Kede Ma, Yuming Fang
This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA). From an actionable perspective, we will first…
Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping
Chenyang Le, Jiebin Yan, Yuming Fang +1
We describe a deep high-dynamic-range (HDR) image tone mapping operator that is computationally efficient and perceptually optimized. We first decompose an HDR image into a normali…
Task-Specific Normalization for Continual Learning of Blind Image Quality Models
Weixia Zhang, Kede Ma, Guangtao Zhai +1
In this paper, we present a simple yet effective continual learning method for blind image quality assessment (BIQA) with improved quality prediction accuracy, plasticity-stability…
Semi-Supervised Deep Ensembles for Blind Image Quality Assessment
Zhihua Wang, Dingquan Li, Kede Ma
Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse." Here we investigate a semi-supervised ensem…
Troubleshooting Blind Image Quality Models in the Wild
Zhihua Wang, Haotao Wang, Tianlong Chen +2
Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When…
Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition
Jiebin Yan, Yu Zhong, Yuming Fang +2
Semantic segmentation is an extensively studied task in computer vision, with numerous methods proposed every year. Thanks to the advent of deep learning in semantic segmentation,…
Continual Learning for Blind Image Quality Assessment
Weixia Zhang, Dingquan Li, Chao Ma +3
The explosive growth of image data facilitates the fast development of image processing and computer vision methods for emerging visual applications, meanwhile introducing novel di…