This is a past event
Generating scientific images (e.g., scientific figures) by hand can often be a time-consuming laborious task, where some popular coding languages such as TikZ come with a steep learning curve. Automatizing this process promises to facilitate and accelerate scientific multimodal content production. In this talk, I will discuss our recent approaches to the problem. In particular, I will talk about (i) AutomaTikz (ICLR 2024) which addresses the problem of generating scientific figures from textual instructions, (ii) DeTikZify (NeurIPS 2024) which allows to generate scientific figures from images or sketches, and (iii) ScImage (ICLR 2025) which provides a template-based benchmark for evaluating multimodal LLMs for instruction-based figure generation. I will also talk about our ongoing research in this context which e.g., addresses the misalignment problem between scientific images and the corresponding captions.
- Speaker
- Steffen Eger
- Venue
- Meston G05 and Microsoft Teams