Multi-Modal Prompt Learning on Blind Image Quality Assessment

arXiv:2404.14949v1 Announce Type: new
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