6 papers
Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment
Runze Hu, Zihao Huang, Xudong Li +3
Human visual perception naturally evaluates image quality across multiple scales, a hierarchical process that existing blind image quality assessment (BIQA) algorithms struggle to…
Few-Shot Image Quality Assessment via Adaptation of Vision-Language Models
Xudong Li, Zihao Huang, Yan Zhang +5
Image Quality Assessment (IQA) remains an unresolved challenge in computer vision due to complex distortions, diverse image content, and limited data availability. Existing Blind I…
Contrastive Local Manifold Learning for No-Reference Image Quality Assessment
Zihao Huang, Runze Hu, Timin Gao +3
Image Quality Assessment (IQA) methods typically overlook local manifold structures, leading to compromised discriminative capabilities in perceptual quality evaluation. To address…
Multi-Modal Prompt Learning on Blind Image Quality Assessment
Wensheng Pan, Timin Gao, Yan Zhang +10
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Currently, leveraging seman…
Weakly Supervised Open-Vocabulary Object Detection
Jianghang Lin, Yunhang Shen, Bingquan Wang +3
Despite weakly supervised object detection (WSOD) being a promising step toward evading strong instance-level annotations, its capability is confined to closed-set categories withi…
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity
Xudong Li, Timin Gao, Runze Hu +9
The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all…