Generating Aesthetic Based Critique For Photographs

Yong Yaw Yeo, John See*, Lai Kuan Wong, Hui Ngo Goh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


The recent surge in deep learning methods across multiple modalities has resulted in an increased interest in image captioning. Most advances in image captioning are still focused on the generation of factual-centric captions, which mainly describe the contents of an image. However, generating captions to provide a meaningful and opinionated critique of photographs is less studied. This paper presents a framework for leveraging aesthetic features encoded from an image aesthetic scorer, to synthesize human-like textual critique via a sequence decoder. Experiments on a large-scale dataset show that the proposed method is capable of producing promising results on relevant metrics relating to semantic diversity and synonymity, with qualitative observations demonstrating likewise. We also suggest the use of Word Mover’s Distance as a semantically intuitive and informative metric for this task.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing
Number of pages5
ISBN (Electronic)9781665441155
Publication statusPublished - 23 Aug 2021
Event28th IEEE International Conference on Image Processing 2021 - Anchorage, United States
Duration: 19 Sept 202122 Sept 2021


Conference28th IEEE International Conference on Image Processing 2021
Abbreviated title2021 IEEE ICIP
Country/TerritoryUnited States
Internet address


  • Aesthetic quality assessment
  • Encoder-decoder network
  • Image captioning
  • Text synthesis
  • Word mover’s distance

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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