  This book is the result of a seminar in which we reviewed multimodal
approaches and attempted to create a solid overview of the field, starting with
the current state-of-the-art approaches in the two subfields of Deep Learning
individually. Further, modeling frameworks are discussed where one modality is
transformed into the other, as well as models in which one modality is utilized
to enhance representation learning for the other. To conclude the second part,
architectures with a focus on handling both modalities simultaneously are
introduced. Finally, we also cover other modalities as well as general-purpose
multi-modal models, which are able to handle different tasks on different
modalities within one unified architecture. One interesting application
(Generative Art) eventually caps off this booklet.
