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Canonical Correlation Inference for Mapping Abstract Scenes to Text

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Nikos Papasarantopoulos

University of Edinburgh


Helen Jiang

Stanford University


Shay Cohen

University of Edinburgh


DOI:

10.1609/aaai.v32i1.11958


Abstract:

We describe a technique for structured prediction, based on canonical correlation analysis. Our learning algorithm finds two projections for the input and the output spaces that aim at projecting a given input and its correct output into points close to each other. We demonstrate our technique on a language-vision problem, namely the problem of giving a textual description to an "abstract scene".