Efficient relative attribute learning using graph neural networks

Zihang Meng, Nagesh Adluru, Hyunwoo J. Kim, Glenn Fung, Vikas Singh

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

4 Citations (Scopus)


A sizable body of work on relative attributes provides evidence that relating pairs of images along a continuum of strength pertaining to a visual attribute yields improvements in a variety of vision tasks. In this paper, we show how emerging ideas in graph neural networks can yield a solution to various problems that broadly fall under relative attribute learning. Our main idea is the observation that relative attribute learning naturally benefits from exploiting the graph of dependencies among the different relative attributes of images, especially when only partial ordering is provided at training time. We use message passing to perform end to end learning of the image representations, their relationships as well as the interplay between different attributes. Our experiments show that this simple framework is effective in achieving competitive accuracy with specialized methods for both relative attribute learning and binary attribute prediction, while relaxing the requirements on the training data and/or the number of parameters, or both.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
EditorsVittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783030012632
Publication statusPublished - 2018
Externally publishedYes
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: 2018 Sept 82018 Sept 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11218 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other15th European Conference on Computer Vision, ECCV 2018


  • Graph neural networks
  • Message passing
  • Multi-task learning
  • Relative attribute learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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