Image Aesthetics Assessment via Learnable Queries

TL;DR

This paper introduces IAA-LQ, a novel method for image aesthetics assessment that uses learnable queries to efficiently extract aesthetic features from pre-trained image encoders, outperforming existing methods.

Contribution

The paper proposes a learnable query-based approach for image aesthetics assessment, reducing training time and attention dispersion issues of previous methods.

Findings

IAA-LQ outperforms state-of-the-art methods by 2.2% SRCC and 2.1% PLCC.

The approach effectively extracts aesthetic features using frozen image encoders.

Extensive experiments validate the advantages of the proposed method.

Abstract

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.