  Functional Distributional Semantics is a recently proposed framework for
learning distributional semantics that provides linguistic interpretability. It
models the meaning of a word as a binary classifier rather than a numerical
vector. In this work, we propose a method to train a Functional Distributional
Semantics model with grounded visual data. We train it on the Visual Genome
dataset, which is closer to the kind of data encountered in human language
acquisition than a large text corpus. On four external evaluation datasets, our
model outperforms previous work on learning semantics from Visual Genome.
