Image Aesthetic Description Based on Semantic Addition Transformer Model

نویسندگان

چکیده

Image aesthetic quality assessment has been a hot research topic in the field of image analysis during last decade. Most recently, people have proposed comment type to describe aesthetics an using text automatically. However, existing works rarely considered description. In this work, we propose novel neural description network framework, named Deep Aesthetic Reviewer (DIAReviewer), based on Semantic Addition Transformer Model, learning Residual Network, and Attention Mechanism single framework. Beyond that, design module compromise feature semantic information focus quality, such as fluency complexity. We introduce new dataset Review Dataset (ARD), which contains one or more comments for each image. Finally, experimental results ARD show that our model outperforms other methods content complexity sentence descriptions.

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ژورنال

عنوان ژورنال: International Journal of Cognitive Informatics and Natural Intelligence

سال: 2022

ISSN: ['1557-3958', '1557-3966']

DOI: https://doi.org/10.4018/ijcini.20211001.oa14