  Successful multimodal search and retrieval requires the automatic
understanding of semantic cross-modal relations, which, however, is still an
open research problem. Previous work has suggested the metrics cross-modal
mutual information and semantic correlation to model and predict cross-modal
semantic relations of image and text. In this paper, we present an approach to
predict the (cross-modal) relative abstractness level of a given image-text
pair, that is whether the image is an abstraction of the text or vice versa.
For this purpose, we introduce a new metric that captures this specific
relationship between image and text at the Abstractness Level (ABS). We present
a deep learning approach to predict this metric, which relies on an autoencoder
architecture that allows us to significantly reduce the required amount of
labeled training data. A comprehensive set of publicly available scientific
documents has been gathered. Experimental results on a challenging test set
demonstrate the feasibility of the approach.
