Emanuele Bugliarello
Visual assistants are becoming ubiquitous, yet their effectiveness varies drastically across languages and cultures. This talk presents an overview of the critical issue of multicultural disparity in image–text models. We'll explore this gap through three lenses: evaluation, training, and generation. First, I'll introduce benchmarks like MaRVL designed to quantify multilingual and multicultural competence. Next, we'll delve into techniques for mitigating these disparities in model training. Finally, we'll examine the emerging challenges and opportunities in multicultural visual generation.
Emanuele Bugliarello is a research scientist at Google DeepMind based in Grenoble, France where he works on improving evaluation and capabilities of multimodal generative models. He completed his PhD in the NLP Section at the University of Copenhagen, while spending time at DeepMind, Google, Mila and Spotify. Previously, he studied computer and communication sciences at EPFL, Tongji University and Politecnico di Torino.
