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 methodologies used in Natural Language Processing (NLP) as well as Computer Vision (CV). Beyond these improvements on single-modality models, large-scale multimodal 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.

Artificial Intelligence (AI) and space are both popular subjects in the current policy climate. AI techniques are being applied to space datasets and accelerating progress in the satellite and space industry through natural language processing, machine vision and advanced analytics. The combination of AI and space could play an integral role in increasing global connectivity and closing the digital divide. AI space services face the same problems as terrestrial AI services. They are exposed to the same policy challenges when delivered through a fibre network as they are when transmitted wirelessly from a satellite.