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

Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting
David Latortue
Moetez Kdayem
Fidel A. Guerrero Peña
Eric Granger
Marco Pedersoli
Object detection models are commonly used for people counting (and localization) in many applications but require a dataset with costly boun… (see more)ding box annotations for training. Given the importance of privacy in people counting, these models rely more and more on infrared images, making the task even harder. In this paper, we explore how weaker levels of supervision affect the performance of deep person counting architectures for image classification and point-level localization. Our experiments indicate that counting people using a convolutional neural network with image-level annotation achieves a level of accuracy that is competitive with YOLO detectors and point-level localization models yet provides a higher frame rate and a simi-lar amount of model parameters. Our code is available at: https://github.com/tortueTortue/IRPeopleCounting.
2024-01-01
2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) (published)
doi.org
arxiv.org
Joint Multimodal Transformer for Dimensional Emotional Recognition in the Wild
Paul Waligora
Muhammad Haseeb Aslam
Muhammad Osama Zeeshan
Soufiane Belharbi
Alessandro Lameiras Koerich
Marco Pedersoli
Simon Bacon
Eric Granger
Audiovisual emotion recognition (ER) in videos has immense potential over unimodal performance. It effectively leverages the inter-and intra… (see more)-modal dependencies between visual and auditory modalities. This work proposes a novel audio-visual emotion recognition system utilizing a joint multimodal transformer architecture with key-based cross-attention. This framework aims to exploit the complementary nature of audio and visual cues (facial expressions and vocal patterns) in videos, leading to superior performance compared to solely relying on a single modality. The proposed model leverages separate backbones for capturing intra-modal temporal dependencies within each modality (audio and visual). Subse-quently, a joint multimodal transformer architecture integrates the individual modality embeddings, enabling the model to effectively capture inter-modal (between audio and visual) and intra-modal (within each modality) relationships. Extensive evaluations on the challenging Affwild2 dataset demonstrate that the proposed model significantly outperforms baseline and state-of-the-art methods in ER tasks.
2024-01-01
arXiv.org (preprint)
doi.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
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
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
Juan A. Rodriguez
Abhay Puri
Shubham Agarwal
Issam Hadj Laradji
Pau Rodriguez
Sai Rajeswar
David Vazquez
Chris Pal
Marco Pedersoli
Scalable Vector Graphics (SVGs) have become integral in modern image rendering applications due to their infinite scalability in resolution,… (see more) versatile usability, and editing capabilities. SVGs are particularly popular in the fields of web development and graphic design. Existing approaches for SVG modeling using deep learning often struggle with generating complex SVGs and are restricted to simpler ones that require extensive processing and simplification. This paper introduces StarVector, a multimodal SVG generation model that effectively integrates Code Generation Large Language Models (CodeLLMs) and vision models. Our approach utilizes a CLIP image encoder to extract visual representations from pixel-based images, which are then transformed into visual tokens via an adapter module. These visual tokens are pre-pended to the SVG token embeddings, and the sequence is modeled by the StarCoder model using next-token prediction, effectively learning to align the visual and code tokens. This enables StarVector to generate unrestricted SVGs that accurately represent pixel images. To evaluate StarVector's performance, we present SVG-Bench, a comprehensive benchmark for evaluating SVG methods across multiple datasets and relevant metrics. Within this benchmark, we introduce novel datasets including SVG-Stack, a large-scale dataset of real-world SVG examples, and use it to pre-train StarVector as a large foundation model for SVGs. Our results demonstrate significant enhancements in visual quality and complexity handling over current methods, marking a notable advancement in SVG generation technology. Code and models: https://github.com/joanrod/star-vector
2023-12-17
ArXiv (preprint)
doi.org
arxiv.org