Explicit reasoning over end-to-end neural architectures for visual question answering

Somak Aditya, Yezhou Yang, Chitta Baral

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

12 Scopus citations

Abstract

Many vision and language tasks require commonsense reasoning beyond data-driven image and natural language processing. Here we adopt Visual Question Answering (VQA) as an example task, where a system is expected to answer a question in natural language about an image. Current state-of-the-art systems attempted to solve the task using deep neural architectures and achieved promising performance. However, the resulting systems are generally opaque and they struggle in understanding questions for which extra knowledge is required. In this paper, we present an explicit reasoning layer on top of a set of penultimate neural network based systems. The reasoning layer enables reasoning and answering questions where additional knowledge is required, and at the same time provides an interpretable interface to the end users. Specifically, the reasoning layer adopts a Probabilistic Soft Logic (PSL) based engine to reason over a basket of inputs: visual relations, the semantic parse of the question, and background ontological knowledge from word2vec and ConceptNet. Experimental analysis of the answers and the key evidential predicates generated on the VQA dataset validate our approach.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages629-637
Number of pages9
ISBN (Electronic)9781577358008
StatePublished - Jan 1 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Aditya, S., Yang, Y., & Baral, C. (2018). Explicit reasoning over end-to-end neural architectures for visual question answering. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 629-637). (32nd AAAI Conference on Artificial Intelligence, AAAI 2018). AAAI press.