Preface
Author: Matthias Aßenmacher
FIGURE 1:
LMU seal (left) style-transferred to Van Gogh’s Sunflower
painting (center) and blended with the prompt - Van Gogh, sunflowers -
via CLIP+VGAN (right).
In the last few years, there have been several breakthroughs in the methodolo-
gies used in Natural Language Processing (NLP) as well as Computer Vision
(CV). Beyond these improvements on single-modality models, large-scale multi-
modal approaches have become a very active area of research.
In this seminar, we reviewed these 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 Chapter 3.1
and Chapter 3.2), as well as models in which one modality is utilized to
enhance representation learning for the other (Chapter 3.3 and Chapter 3.4).
To conclude the second part, architectures with a focus on handling both
modalities simultaneously are introduced (Chapter 3.5). Finally, we also cover
other modalities (Chapter 4.1 and Chapter 4.2) as well as general-purpose
multi-modal models (Chapter 4.3), which are able to handle different tasks on
different modalities within one unified architecture. One interesting application
(Generative Art, Chapter 4.4) eventually caps off this booklet.
v
