Portrait of Marco Pedersoli

Marco Pedersoli

Affiliate Member
Associate Professor, École de technologie suprérieure
Research Topics
Building Energy Management Systems
Computer Vision
Deep Learning
Generalization
Generative Models
Multimodal Learning
Representation Learning
Robustness
Satellite Imagery
Vision and Language
Weak Supervision
Google Scholar
LinkedIn

Biography

I am an Associate Professor at ÉTS Montreal, a member of LIVIA (le Laboratoire d'Imagerie, Vision et Intelligence Artificielle), and part of the International Laboratory of Learning Systems (ILLS). I am also a member of ELLIS, the European network of excellence in AI. Since 2021, I have co-held the Distech Industrial Research Chair on Embedded Neural Networks for Connected Building Control.

My research centers on Deep Learning methods and algorithms, with a focus on visual recognition, and the automatic interpretation and understanding of images and videos. A key objective of my work is to advance machine intelligence by minimizing two critical factors: computational load and the need for human supervision. These reductions are essential for scalable AI, enabling more efficient, adaptive, and embedded systems. In my recent work, I have contributed to developing neural networks for smart buildings, integrating AI-driven solutions to enhance energy efficiency and comfort in intelligent environments.

Publications

StarVector: Generating Scalable Vector Graphics Code from Images and Text
Juan A. Rodriguez
Abhay Puri
Shubham Agarwal
Issam Hadj Laradji
Pau Rodriguez
Sai Rajeswar
David Vazquez
Chris Pal
Marco Pedersoli
Scalable Vector Graphics (SVGs) are vital for modern image rendering due to their scalability and versatility. Previous SVG generation metho… (see more)ds have focused on curve-based vectorization, lacking semantic understanding, often producing artifacts, and struggling with SVG primitives beyond path curves. To address these issues, we introduce StarVector, a multimodal large language model for SVG generation. It performs image vectorization by understanding image semantics and using SVG primitives for compact, precise outputs. Unlike traditional methods, StarVector works directly in the SVG code space, leveraging visual understanding to apply accurate SVG primitives. To train StarVector, we create SVG-Stack, a diverse dataset of 2M samples that enables generalization across vectorization tasks and precise use of primitives like ellipses, polygons, and text. We address challenges in SVG evaluation, showing that pixel-based metrics like MSE fail to capture the unique qualities of vector graphics. We introduce SVG-Bench, a benchmark across 10 datasets, and 3 tasks: Image-to-SVG, Text-to-SVG generation, and diagram generation. Using this setup, StarVector achieves state-of-the-art performance, producing more compact and semantically rich SVGs.
2023-12-17
ArXiv (preprint)
arxiv.org
arxiv.org
StarVector: Generating Scalable Vector Graphics Code from Images and Text
Juan A. Rodriguez
Abhay Puri
Shubham Agarwal
Issam Hadj Laradji
Pau Rodriguez
Sai Rajeswar
David Vazquez
Chris Pal
Marco Pedersoli
2023-12-17
ArXiv (preprint)
arxiv.org
arxiv.org
DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization
Shakeeb Murtaza
Soufiane Belharbi
Marco Pedersoli
Aydin Sarraf
Eric Granger
2023-12-01
Image and Vision Computing (published)
doi.org
arxiv.org