English
资源说明：Visual question answering is fundamentally compositional
in nature—a question like where is the dog? shares
substructure with questions like what color is the dog? and
where is the cat? This paper seeks to simultaneously exploit
the representational capacity of deep networks and the compositional
linguistic structure of questions. We describe a
procedure for constructing and learning neural module networks,
which compose collections of jointly-trained neural
“modules” into deep networks for question answering. Our
approach decomposes questions into their linguistic substructures,
and uses these structures to dynamically instantiate
modular networks (with reusable components for recognizing
dogs, classifying colors, etc.). The resulting compound
networks are jointly trained. We evaluate our approach
on two challenging datasets for visual question answering,
achieving state-of-the-art results on both the VQA
natural image dataset and a new dataset of complex questions
about abstract shapes.
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