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Visual Question Generation for Class Acquisition of Unknown Objects

  • Kohei UeharaEmail author
  • Antonio Tejero-De-Pablos
  • Yoshitaka Ushiku
  • Tatsuya Harada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11216)

Abstract

Traditional image recognition methods only consider objects belonging to already learned classes. However, since training a recognition model with every object class in the world is unfeasible, a way of getting information on unknown objects (i.e., objects whose class has not been learned) is necessary. A way for an image recognition system to learn new classes could be asking a human about objects that are unknown. In this paper, we propose a method for generating questions about unknown objects in an image, as means to get information about classes that have not been learned. Our method consists of a module for proposing objects, a module for identifying unknown objects, and a module for generating questions about unknown objects. The experimental results via human evaluation show that our method can successfully get information about unknown objects in an image dataset. Our code and dataset are available at https://github.com/mil-tokyo/vqg-unknown.

Keywords

Visual question generation Unknown object recognition Unknown object class acquisition Real world recognition 

Notes

Acknowledgement

This work was supported by JST CREST Grant Number JPMJCR1403, Japan.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.The University of TokyoTokyoJapan
  2. 2.RIKENTokyoJapan

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