March 18, 2024Open Access

Image Aesthetics Assessment Via Learnable Queries

Puntos clave

Image aesthetics assessment improves with the use of learnable queries, leading to better aesthetic evaluations.
The IAA-LQ method outperforms traditional methods by 2.2% in SRCC and 2.1% in PLCC metrics, demonstrating significant advantages.
Assessment involves using a frozen image encoder to extract features without extensive retraining of vision models for aesthetic evaluation and scoring, emphasizing efficiency in process and accuracy in results overall across diverse images in real-world settings and applications that require consistent aesthetic evaluations and rankings in multimedia contexts or fields like marketing, design, and content creation processes where understanding image appeal is key to success and audience engagement strategies.

Resumen

Image aesthetics assessment (IAA) aims to estimate the aesthetics of images. Depending on the content of an image, diverse criteria need to be selected to assess its aesthetics. Existing works utilize pre-trained vision backbones based on content knowledge to learn image aesthetics. However, training those backbones is time-consuming and suffers from attention dispersion. Inspired by learnable queries in vision-language alignment, we propose the Image Aesthetics Assessment via Learnable Queries (IAA-LQ) approach. It adapts learnable queries to extract aesthetic features from pre-trained image features obtained from a frozen image encoder. Extensive experiments on real-world data demonstrate the advantages of IAA-LQ, beating the best state-of-the-art method by 2.2% and 2.1% in terms of SRCC and PLCC, respectively.

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