  The explosive increase of multimodal data makes a great demand in many
cross-modal applications that follow the strict prior related assumption. Thus
researchers study the definition of cross-modal correlation category and
construct various classification systems and predictive models. However, those
systems pay more attention to the fine-grained relevant types of cross-modal
correlation, ignoring lots of implicit relevant data which are often divided
into irrelevant types. What's worse is that none of previous predictive models
manifest the essence of cross-modal correlation according to their definition
at the modeling stage. In this paper, we present a comprehensive analysis of
the image-text correlation and redefine a new classification system based on
implicit association and explicit alignment. To predict the type of image-text
correlation, we propose the Association and Alignment Network according to our
proposed definition (namely AnANet) which implicitly represents the global
discrepancy and commonality between image and text and explicitly captures the
cross-modal local relevance. The experimental results on our constructed new
image-text correlation dataset show the effectiveness of our model.
