Abstract. Visual dialog entails answering a series of questions grounded
in an image, using dialog history as context. In addition to the challenges
found in visual question answering (VQA), which can be seen as oneround dialog, visual dialog encompasses several more. We focus on one
such problem called visual coreference resolution that involves determining which words, typically noun phrases and pronouns, co-refer to the
same entity/object instance in an image. This is crucial, especially for
pronouns (e.g., ‘it’), as the dialog agent must first link it to a previous
coreference (e.g., ‘boat’), and only then can rely on the visual grounding
of the coreference ‘boat’ to reason about the pronoun ‘it’. Prior work
(in visual dialog) models visual coreference resolution either (a) implicitly via a memory network over history, or (b) at a coarse level for the
entire question; and not explicitly at a phrase level of granularity. In
this work, we propose a neural module network architecture for visual
dialog by introducing two novel modules—Refer and Exclude—that perform explicit, grounded, coreference resolution at a finer word level. We
demonstrate the effectiveness of our model on MNIST Dialog, a visually
simple yet coreference-wise complex dataset, by achieving near perfect
accuracy, and on VisDial, a large and challenging visual dialog dataset
on real images, where our model outperforms other approaches, and is
more interpretable, grounded, and consistent qualitatively