  We propose a generalized class of multimodal fusion operators for the task of
visual question answering (VQA). We identify generalizations of existing
multimodal fusion operators based on the Hadamard product, and show that
specific non-trivial instantiations of this generalized fusion operator exhibit
superior performance in terms of OpenEnded accuracy on the VQA task. In
particular, we introduce Nonlinearity Ensembling, Feature Gating, and
post-fusion neural network layers as fusion operator components, culminating in
an absolute percentage point improvement of $1.1\%$ on the VQA 2.0 test-dev set
over baseline fusion operators, which use the same features as input. We use
our findings as evidence that our generalized class of fusion operators could
lead to the discovery of even superior task-specific operators when used as a
search space in an architecture search over fusion operators.
