The following demonstrators stood out in particular: 

fAIritale (see Figure 1) - creates short bedtime stories based on anonymized real children's drawings. For each story, a drawing is annotated with a few keywords. Then, using this text and the image-to-image model Stable Diffusion, a similar-looking but anonymized illustration is created. The matching story is generated by GPT-3.5 Turbo using the OpenAI interface. Both model queries include prompt engineering to generate results that match the story theme.

Improving object detection with LLMs (see Figure 2) - uses GPT-3 to increase the robustness of object detectors. For this purpose, a standard object detector (YOLOv5) is fed with images that are prepared to provoke false detections (a so-called "PGD Adversarial Attack"). The list of detected objects is then sent to GPT-3 with the question whether all detections fit into the same scene based on the context. Thus, inappropriate detections can be identified and subsequently removed.

Image Freedom: A Machine Learning Approach to generate royalty free Images see Figure 3) - generates royalty free images that have similar content to another potentially non-royalty free image.  To do this, an image-to-text model is first used to create a detailed description of the image; the resulting text is then fed into a text-to-image model. This creates a new image that is visually similar, but also represents the original content. This application could be conveniently accessed via cell phone using a Telegram bot.