no code implementations • 29 Jul 2022 • Yiting Lu, Xin Li, Jianzhao Liu, Zhibo Chen
Specifically, we find a more compact and reliable space i. e., feature style space for perception-oriented UDA based on an interesting/amazing observation, that the feature style (i. e., the mean and variance) of the deep layer in DNNs is exactly associated with the quality score in NR-IQA.
no code implementations • 17 Jul 2022 • Jianzhao Liu, Xin Li, Shukun An, Zhibo Chen
Thanks to the development of unsupervised domain adaptation (UDA), some works attempt to transfer the knowledge from a label-sufficient source domain to a label-free target domain under domain shift with UDA.
Blind Image Quality Assessment Unsupervised Domain Adaptation
1 code implementation • 9 May 2022 • Jianzhao Liu, Xin Li, Yanding Peng, Tao Yu, Zhibo Chen
In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space.
no code implementations • 30 Sep 2020 • Yingxue Pang, Xin Li, Xin Jin, Yaojun Wu, Jianzhao Liu, Sen Liu, Zhibo Chen
Specifically, we extract different frequencies of the LR image and pass them to a channel attention-grouped residual dense network (CA-GRDB) individually to output corresponding feature maps.
no code implementations • ECCV 2020 • Jianzhao Liu, Jianxin Lin, Xin Li, Wei Zhou, Sen Liu, Zhibo Chen
Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.