Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171829
Title: Multi-level transitional contrast learning for personalized image aesthetics assessment
Authors: Yang, Zhichao
Li, Leida
Yang, Yuzhe
Li, Yaqian
Lin, Weisi
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Source: Yang, Z., Li, L., Yang, Y., Li, Y. & Lin, W. (2023). Multi-level transitional contrast learning for personalized image aesthetics assessment. IEEE Transactions On Multimedia. https://dx.doi.org/10.1109/TMM.2023.3290479
Journal: IEEE Transactions on Multimedia
Abstract: Personalized image aesthetics assessment (PIAA) is aimed at modeling the unique aesthetic preferences of individuals, based on which personalized aesthetic scores are predicted. People have different standards for image aesthetics, and accordingly, images rated at the same aesthetic level by different users explicitly reveal their aesthetic preferences. However, previous PIAA models treat each individual as an isolated optimization target, failing to take full advantage of the contrastive information among users. Further, although people's aesthetic preferences are unique, they still share some commonalities, meaning that PIAA models could be built on the basis of generic aesthetics. Motivated by the above facts, this paper presents a Multi-level Transitional Contrast Learning (MTCL) framework for PIAA by transiting features from generic aesthetics to personalized aesthetics via contrastive learning. First, a generic image aesthetics assessment network is pre-trained to learn the common aesthetic features. Then, image sets rated to have the same aesthetic levels by different users are employed to learn the differentiated aesthetic features through multiple level-wise contrast learning based on the generic aesthetic features. Finally, a target user's PIAA model is built by integrating generic and differentiated aesthetic features. Extensive experiments on four benchmark PIAA databases demonstrate that the proposed MTCL model outperforms the state-of-the-arts.
URI: https://hdl.handle.net/10356/171829
ISSN: 1520-9210
DOI: 10.1109/TMM.2023.3290479
Schools: School of Computer Science and Engineering 
Rights: © 2023 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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