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Efficient Relative Attribute Learning Using Graph Neural Networks

  • Zihang MengEmail author
  • Nagesh Adluru
  • Hyunwoo J. Kim
  • Glenn Fung
  • Vikas Singh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11218)

Abstract

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.

Keywords

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

Notes

Acknowledgment

This work was partially supported by funding from American Family Insurance and UW CPCP AI117924. Partial support from NSF CAREER award 1252725, NIH grants R01 AG040396, BRAIN Initiative R01-EB022883, and Waisman IDDRC U54-HD090256 is also acknowledged. The authors are grateful to Haoliang Sun for help with illustrations and other suggestions/advice on this project. The code will appear in https://github.com/zihangm/RAL_GNN.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zihang Meng
    • 1
    Email author
  • Nagesh Adluru
    • 1
  • Hyunwoo J. Kim
    • 1
  • Glenn Fung
    • 2
  • Vikas Singh
    • 1
  1. 1.University of Wisconsin – MadisonMadisonUSA
  2. 2.American Family InsuranceMadisonUSA

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