Tom Monnier

PhD student at Imagine - ENPC

6-8 Av Blaise Pascal - Cité Descartes
77455 Marne-la-Vallée, France

Email: tom dot monnier at enpc dot fr

I am a second-year PhD student in the Imagine research group at École des Ponts ParisTech (ENPC) under the supervision of Mathieu Aubry. Before that, I received my engineering degree in mathematics and computer science from Mines ParisTech in 2019.

I am interested in computer vision, machine learning and deep learning. My research currently focuses on solving computer vision tasks without manual annotations, through automatically generated data, self-supervised learning techniques or unsupervised algorithms.



Representing Shape Collections with Alignment-Aware Linear Models
Romain Loiseau, Tom Monnier, Mathieu Aubry, Loïc Landrieu
arXiv 2021
paper | webpage | code | bibtex

We characterize 3D shapes as affine transformations of linear families learned without supervision, and showcase its advantages on large shape collections.

Unsupervised Layered Image Decomposition into Object Prototypes
Tom Monnier, Elliot Vincent, Jean Ponce, Mathieu Aubry
ICCV 2021
paper | webpage | code | video | slides | bibtex

An unsupervised learning framework to decompose images into object layers modeled as transformed instances of prototypical images called sprites.

Deep Transformation-Invariant Clustering
Tom Monnier, Thibault Groueix, Mathieu Aubry
NeurIPS 2020 (oral presentation)
paper | webpage | code | video | slides | bibtex

A simple and interpretable approach to clustering that jointly learns prototypes and prototype transformations to match data.

docExtractor: An off-the-shelf historical document element extraction
Tom Monnier, Mathieu Aubry
ICFHR 2020 (oral presentation)
paper | webpage | code | demo | video | slides | bibtex

Leveraging synthetic data and segmentation networks for generic element extraction in real historical document images.

A web application for watermark recognition
O. Bounou, T. Monnier, I. Pastrolin, X. Shen, C. Bénévent and others
JDMDH 2020
paper | web application | related paper (Shen et al.)

New public web application dedicated to automatic watermark recognition.


Work experience

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Last update: September 2021