Personalized Image Aesthetics Assessment with Rich Attributes

Yang, Yuzhe; Xu, Liwu; Li, Leida; Qie, Nan; Li, Yaqian; Zhang, Peng; Guo, Yandong

doi:10.1109/cvpr52688.2022.01924

Cited by 27 publications

(24 citation statements)

References 19 publications

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“…Personalized Image Aesthetics Assessment. Personalized Image Aesthetics Assessment (PIAA) [12,22,49,52,53] aims to learn to assess the aesthetic quality (or score) of images by taking into account the users' aesthetic preferences. PIAA is a recent popular topic which is derived from the Generic Image Aesthetic Assessment (GIAA) [10,51].…”

Section: Related Workmentioning

confidence: 99%

“…In this kind of task, the personalized aesthetics models are optimized to quickly adapt to a new user's aesthetic preference, and these PIAA models may fail to capture personalized aesthetics [52]. To this end, recent PIAA works [23,44,49,52] based on metalearning paradigms are proposed to tackle this problem. Although the promising PIAA performance is achieved, most methods still have complex training frameworks [21,44,52] that are not suitable for deployment in practice.…”

Section: Related Workmentioning

confidence: 99%

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MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction

Lin,

Shen,

Yin

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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Predicting individual aesthetic preferences holds significant practical applications and academic implications for human society. However, existing studies mainly focus on learning and predicting the commonality of facial attractiveness, with little attention given to Personalized Facial Beauty Prediction (PFBP). PFBP aims to develop a machine that can adapt to individual aesthetic preferences with only a few images rated by each user. In this paper, we formulate this task from a meta-learning perspective that each user corresponds to a meta-task. To address such PFBP task, we draw inspiration from the human aesthetic mechanism that visual aesthetics in society follows a Gaussian distribution, which motivates us to disentangle user preferences into a commonality and an individuality part. To this end, we propose a novel MetaFBP framework, in which we devise a universal feature extractor to capture the aesthetic commonality and then optimize to adapt the aesthetic individuality by shifting the decision boundary of the predictor via a meta-learning mechanism. Unlike conventional meta-learning methods that may struggle with slow adaptation or overfitting to tiny support sets, we propose a novel approach that optimizes a high-order predictor for fast adaptation. In order to validate the performance of the proposed method, we build several PFBP benchmarks by using existing facial beauty prediction datasets rated by numerous users. Extensive experiments on these benchmarks demonstrate the effectiveness of the proposed MetaFBP method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction

Lin,

Shen,

Yin

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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“…Typically, IAA approaches can be classified into two broad categories: generic image aesthetics assessment (GIAA) and personalized image aesthetics assessment (PIAA) [8]. As the name indicates, GIAA aims to infer the aesthetic experiences perceived by most people [9], whereas PIAA is designed for the aesthetic ratings of a certain individual user for images [10]. Early IAA methods mainly leveraged general attributes in photography and artistic painting (e.g., composition, color, light) to measure the aesthetics of images for most people (GIAA) [2].…”

Section: Introductionmentioning

confidence: 99%

“…PIAA is a user-oriented approach that can only utilize the annotated data provided by each individual user to build a PIAA model [10]. Usually, the number of annotated data provided by a user is limited, which is unable to directly train an efficient PIAA model based on a deep learning framework.…”

Section: Introductionmentioning

confidence: 99%

“…We can observe that the generic (average) aesthetics is closely related to multiple attributes, and these attributes jointly influence most users' aesthetic experience of images. Besides, the aesthetic differences among individual users are usually affected by their own subjective attributes [10]. As shown in Figure 1b, the subjective attributes of the two users are quite different.…”

Section: Introductionmentioning

confidence: 99%

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Personalized Image Aesthetics Assessment via Multi-Attribute Interactive Reasoning

et al. 2022

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Due to the subjective nature of people’s aesthetic experiences with respect to images, personalized image aesthetics assessment (PIAA), which can simulate the aesthetic experiences of individual users to estimate images, has received extensive attention from researchers in the computational intelligence and computer vision communities. Existing PIAA models are usually built on prior knowledge that directly learns the generic aesthetic results of images from most people or the personalized aesthetic results of images from a large number of individuals. However, the learned prior knowledge ignores the mutual influence of the multiple attributes of images and users in their personalized aesthetic experiences. To this end, this paper proposes a personalized image aesthetics assessment method via multi-attribute interactive reasoning. Different from existing PIAA models, the multi-attribute interaction constructed from both images and users is used as more effective prior knowledge. First, we designed a generic aesthetics extraction module from the perspective of images to obtain the aesthetic score distribution and multiple objective attributes of images rated by most users. Then, we propose a multi-attribute interactive reasoning network from the perspective of users. By interacting multiple subjective attributes of users with multiple objective attributes of images, we fused the obtained multi-attribute interactive features and aesthetic score distribution to predict personalized aesthetic scores. Experimental results on multiple PIAA datasets demonstrated our method outperformed state-of-the-art PIAA methods.

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