Multi-modal texts are abundant and diverse in structure, yet Language & Vision research of these naturally occurring texts has mostly focused on genres that are comparatively light on text, like tweets. In this paper, we discuss the challenges and potential benefits of a L&V framework that explicitly models referential relations, taking Wikipedia articles about buildings as an example. We briefly survey existing related tasks in L&V and propose multi-modal information extraction as a general direction for future research.

In this paper, I will describe a system that was developed for the task of Visual Question Answering. The system uses the rich type universe of Type Theory with Records (TTR) to model both the utterances about the image, the image itself and classifications made related to the two. At its most basic, the decision of whether any given predicate can be assigned to an object in the image is delegated to a CNN. Consequently, images can be judged as evidence for propositions. The end result is a model whose application of perceptual classifiers to a given image is guided by the accompanying utterance.