  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".
