Personality-assisted Multi-task Learning for Generic and Personalized Image Aesthetics Assessment.

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ID: 88072
2020
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Ranked #4 of 51 articles by views in ieee transactions on image processing : a publication of the ieee signal processing society

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Abstract
Traditional image aesthetics assessment (IAA) approaches mainly predict the average aesthetic score of an image. However, people tend to have different tastes on image aesthetics, which is mainly determined by their subjective preferences. As an important subjective trait, personality is believed to be a key factor in modeling individual's subjective preference. In this paper, we present a personality-assisted multi-task deep learning framework for both generic and personalized image aesthetics assessment. The proposed framework comprises two stages. In the first stage, a multi-task learning network with shared weights is proposed to predict the aesthetics distribution of an image and Big-Five (BF) personality traits of people who like the image. The generic aesthetics score of the image can be generated based on the predicted aesthetics distribution. In order to capture the common representation of generic image aesthetics and people's personality traits, a Siamese network is trained using aesthetics data and personality data jointly. In the second stage, based on the predicted personality traits and generic aesthetics of an image, an inter-task fusion is introduced to generate individual's personalized aesthetic scores on the image. The performance of the proposed method is evaluated using two public image aesthetics databases. The experimental results demonstrate that the proposed method outperforms the state-of-the-arts in both generic and personalized IAA tasks.
Reference Key
li2020personalityassistedieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Li, Leida;Zhu, Hancheng;Zhao, Sicheng;Ding, Guiguang;Lin, Weisi;
Journal ieee transactions on image processing : a publication of the ieee signal processing society
Year 2020
DOI
10.1109/TIP.2020.2968285
URL
Keywords
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