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- W4281747478 abstract "Personalized image esthetics assessment (PIAA) models user's personalized esthetic preference and predicts a unique esthetic score for the user, which has become a hot topic due to its usefulness in social media, album curation, etc. People's esthetic preference is typically determined by a mixture of diversified factors, which makes it extremely challenging to model. Intuitively, people with different personalities have distinct preferences for various photographic contents, while image content and esthetic attributes are always tightly coupled. Motivated by this, this paper presents a content-attribute preference (CAP) framework for PIAA, which models the intricate relationship between user personality and image “content-attribute” coupling features. Specifically, a multi-task learning network is utilized to extract “content-attribute” coupling features, and another parallel network is used to extract personality features. Then, a personalized esthetic prior model is trained to represent the content-attribute preferences of people with different personalities based on the cross attention mechanism. Finally, a PIAA model is obtained by fine-tuning the esthetic prior model using target user data. Extensive experiments on three public databases demonstrate that the proposed CAP model outperforms the state-of-the-arts in terms of both prediction performance and generalization ability." @default.
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- W4281747478 date "2022-08-01" @default.
- W4281747478 modified "2023-10-16" @default.
- W4281747478 title "Modeling content-attribute preference for personalized image esthetics assessment" @default.
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- W4281747478 doi "https://doi.org/10.1016/j.imavis.2022.104505" @default.
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