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Multi-Modal Prompt Learning on Blind Image Quality Assessment
April 24, 2024, 4:45 a.m. | Wensheng Pan, Timin Gao, Yan Zhang, Runze Hu, Xiawu Zheng, Enwei Zhang, Yuting Gao, Yutao Liu, Yunhang Shen, Ke Li, Shengchuan Zhang, Liujuan Cao, Ron
cs.CV updates on arXiv.org arxiv.org
Abstract: Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Currently, leveraging semantic information to enhance IQA is a crucial research direction. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. However, the generalist nature of these pre-trained Vision-Language (VL) models often renders them suboptimal for IQA-specific tasks. Recent approaches …
abstract annotated data arxiv assessment benefit blind clip cs.cv data image information modal multi-modal objects prompt prompt learning quality research semantic text them type types
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