Google Research

Visualizing Semantic Walks

NeurIPS-2022 Workshop on Machine Learning for Creativity and Design, (to appear)


An embedding space trained from both a large language model and vision model contains semantic aspects of both and provides connections between words, images, concepts, and styles. This paper visualizes characteristics and relationships in this semantic space. We traverse multi-step paths in a derived semantic graph to reveal hidden connections created from the immense amount of data used to create these models. We specifically examine these relationships in the domain of painters, their styles, and their subjects. Additionally, we present a novel, non-linear sampling technique to create informative visualization of semantic graph transitions.

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work