  Referring expression comprehension (REC) aims to localize a text-related
region in a given image by a referring expression in natural language. Existing
methods focus on how to build convincing visual and language representations
independently, which may significantly isolate visual and language information.
In this paper, we argue that for REC the referring expression and the target
region are semantically correlated and subject, location and relationship
consistency exist between vision and language.On top of this, we propose a
novel approach called MutAtt to construct mutual guidance between vision and
language, which treat vision and language equally thus yield compact
information matching. Specifically, for each module of subject, location and
relationship, MutAtt builds two kinds of attention-based mutual guidance
strategies. One strategy is to generate vision-guided language embedding for
the sake of matching relevant visual feature. The other reversely generates
language-guided visual feature to match relevant language embedding. This
mutual guidance strategy can effectively guarantees the vision-language
consistency in three modules. Experiments on three popular REC datasets
demonstrate that the proposed approach outperforms the current state-of-the-art
methods.
