Canonical correlation inference for mapping abstract scenes to text
Proceedings of the AAAI Conference on Artificial Intelligence, 2018•ojs.aaai.org
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".
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".
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".
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