Residual Attention Network vs Real Attention on Aesthetic Assessment

Ranju Mandal, Susanne Becken, Rod M. Connolly, Bela Stantic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Photo aesthetics assessment is a challenging problem. Deep Convolutional Neural Network (CNN)-based algorithms have achieved promising results for aesthetics assessment in recent times. Lately, few efficient and effective attention-based CNN architectures are proposed that improve learning efficiency by adaptively adjusts the weight of each patch during the training process. In this paper, we investigate how real human attention affects instead of CNN-based synthetic attention network architecture in image aesthetic assessment. A dataset consists of a large number of images along with eye-tracking information has been developed using an eye-tracking device ( ) power by sensor technology for our research, and it will be the first study of its kind in image aesthetic assessment. We adopted a Residual Attention Network and ResNet architectures which achieve state-of-the-art performance image recognition tasks on benchmark datasets. We report our findings on photo aesthetics assessment with two sets of datasets consist of original images and images with masked attention patches, which demonstrates higher accuracy when compared to the state-of-the-art methods.

Original languageEnglish
Title of host publicationRecent Challenges in Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings
EditorsTzung-Pei Hong, Krystian Wojtkiewicz, Rathachai Chawuthai, Pawel Sitek
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9789811616846
Publication statusPublished - 2021
Event13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021 - Phuket, Thailand
Duration: 7 Apr 202110 Apr 2021

Publication series

NameCommunications in Computer and Information Science
Volume1371 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021


  • Aesthetic scoring
  • Deep learning
  • Great Barrier Reef
  • Image aesthetic evaluation
  • Photo aesthetic assessment


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