Estimating the information gap between textual and visual representations

To convey a complex matter, it is often beneficial to leverage two or more modalities. For example, slides are utilized to supplement an oral presentation, or photographs, drawings, figures, etc. are exploited in online news or scientific publications to complement textual information. However, the utilization of different modalities and their interrelations can be quite diverse. Sometimes, the transfer of information or knowledge may even be not eased, for instance, in case of contradictory information. The variety of possible interrelations of textual and graphical information and the question, how they can be described and automatically estimated have not been addressed yet by previous work. In this paper, we present several contributions to close this gap. First, we introduce two measures to describe two different dimensions of cross-modal interrelations: cross-modal mutual information (CMI) and semantic correlation (SC). Second, two novel deep learning systems are suggested to estimate CMI and SC of textual and visual information. The first deep neural network consists of an autoencoder that maps images and texts onto a multimodal embedding space. This representation is then exploited in order to train classifiers for SC and CMI. An advantage of this representation is that only a small set of labeled training examples is required for the supervised learning process. Third, three different and large datasets are combined for autoencoder training to increase the diversity of (unlabeled) image-text pairs such that they properly capture the broad range of possible interrelations. Fourth, experimental results are reported for a challenging dataset. Finally, we discuss several applications for the proposed system and outline areas for future work.