  A large amount of research about multimodal inference across text and vision
has been recently developed to obtain visually grounded word and sentence
representations. In this paper, we use logic-based representations as unified
meaning representations for texts and images and present an unsupervised
multimodal logical inference system that can effectively prove entailment
relations between them. We show that by combining semantic parsing and theorem
proving, the system can handle semantically complex sentences for
visual-textual inference.
