938 citations · 1.4k across the 9 of their papers we have counts for
9 papers
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
Weixia Zhang, Guangtao Zhai, Ying Wei +2
We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information. We develop a general and automate…
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
Weixia Zhang, Dingquan Li, Xiongkuo Min +4
No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA mode…
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception
Bowen Li, Weixia Zhang, Meng Tian +2
Perceptual quality assessment of the videos acquired in the wilds is of vital importance for quality assurance of video services. The inaccessibility of reference videos with prist…
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…
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…
Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning
Weixia Zhang, Chao Ma, Qi Wu +1
The emerging vision-and-language navigation (VLN) problem aims at learning to navigate an agent to the target location in unseen photo-realistic environments according to the given…
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild
Weixia Zhang, Kede Ma, Guangtao Zhai +1
Performance of blind image quality assessment (BIQA) models has been significantly boosted by end-to-end optimization of feature engineering and quality regression. Nevertheless, d…
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
Weixia Zhang, Kede Ma, Jia Yan +2
We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions. Our model consists of two convolutional neural net…
Learning to Blindly Assess Image Quality in the Laboratory and Wild
Weixia Zhang, Kede Ma, Guangtao Zhai +1
Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically disto…