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A Dataset and Architecture for Visual Reasoning with a Working Memory

  • Guangyu Robert Yang
  • Igor Ganichev
  • Xiao-Jing Wang
  • Jonathon Shlens
  • David Sussillo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11214)

Abstract

A vexing problem in artificial intelligence is reasoning about events that occur in complex, changing visual stimuli such as in video analysis or game play. Inspired by a rich tradition of visual reasoning and memory in cognitive psychology and neuroscience, we developed an artificial, configurable visual question and answer dataset (COG) to parallel experiments in humans and animals. COG is much simpler than the general problem of video analysis, yet it addresses many of the problems relating to visual and logical reasoning and memory – problems that remain challenging for modern deep learning architectures. We additionally propose a deep learning architecture that performs competitively on other diagnostic VQA datasets (i.e. CLEVR) as well as easy settings of the COG dataset. However, several settings of COG result in datasets that are progressively more challenging to learn. After training, the network can zero-shot generalize to many new tasks. Preliminary analyses of the network architectures trained on COG demonstrate that the network accomplishes the task in a manner interpretable to humans.

Keywords

Visual reasoning Visual question answering Recurrent network Working memory 

Supplementary material

474197_1_En_44_MOESM1_ESM.pdf (2.1 mb)
Supplementary material 1 (pdf 2152 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Guangyu Robert Yang
    • 1
    • 3
  • Igor Ganichev
    • 2
  • Xiao-Jing Wang
    • 1
  • Jonathon Shlens
    • 2
  • David Sussillo
    • 2
  1. 1.Center for Neural ScienceNew York UniversityNew YorkUSA
  2. 2.Google BrainMountain ViewUSA
  3. 3.Department of NeuroscienceColumbia UniversityNew YorkUSA

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