Reasoning about Object Affordances in a Knowledge Base Representation

  • Yuke Zhu
  • Alireza Fathi
  • Li Fei-Fei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

Reasoning about objects and their affordances is a fundamental problem for visual intelligence. Most of the previous work casts this problem as a classification task where separate classifiers are trained to label objects, recognize attributes, or assign affordances. In this work, we consider the problem of object affordance reasoning using a knowledge base representation. Diverse information of objects are first harvested from images and other meta-data sources. We then learn a knowledge base (KB) using a Markov Logic Network (MLN). Given the learned KB, we show that a diverse set of visual inference tasks can be done in this unified framework without training separate classifiers, including zero-shot affordance prediction and object recognition given human poses.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yuke Zhu
    • 1
  • Alireza Fathi
    • 1
  • Li Fei-Fei
    • 1
  1. 1.Computer Science DepartmentStanford UniversityUSA

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