Exploring CLIP for Assessing the Look and Feel of Images

@article{Wang2022ExploringCF,
  title={Exploring CLIP for Assessing the Look and Feel of Images},
  author={Jianyi Wang and Kelvin C. K. Chan and Chen Change Loy},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.12396}
}
. Measuring the perception of visual content is a long-standing problem in computer vision. Many mathematical models have been devel-oped to evaluate the look or quality of an image. Despite the effectiveness of such tools in quantifying degradations such as noise and blurriness lev-els, such quantification is loosely coupled with human language. When it comes to more abstract perception about the feel of visual content, existing methods can only rely on supervised models that are explicitly… 

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