Publications by Thomas Grégoire Stegmüller

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

EPFL2025

A Simple Framework for Open-Vocabulary Zero-Shot Segmentation

Jean-Philippe Thiran, Thomas Grégoire Stegmüller, Seyedbehzad Bozorgtabar

Zero-shot classification capabilities naturally arise in models trained within a vision-language contrastive framework. Despite their classification prowess, these models struggle in dense tasks like zero-shot open-vocabulary segmentation. This deficiency ...

International Conference on Learning Representations, ICLR2025

CrOC: Cross-View Online Clustering for Dense Visual Representation Learning

Jean-Philippe Thiran, Thomas Grégoire Stegmüller, Seyedbehzad Bozorgtabar

Learning dense visual representations without labels is an arduous task and more so from scene-centric data. We propose to tackle this challenging problem by proposing a Cross-view consistency objective with an Online Clustering mechanism (CrOC) to discove ...

Los Alamitos2023

Adaptive Similarity Bootstrapping for Self-Distillation based Representation Learning

Jean-Philippe Thiran, Thomas Grégoire Stegmüller, Seyedbehzad Bozorgtabar

Most self-supervised methods for representation learning leverage a cross-view consistency objective i.e. they maximize the representation similarity of a given image's augmented views. Recent work NNCLR goes beyond the cross- view paradigm and uses positi ...

Ieee Computer Soc2023

Telecytologic diagnosis of cervical smears for triage of self-sampled human papillomavirus–positive women in a resource-limited setting: concept development before implementation

Jean-Philippe Thiran, Thomas Grégoire Stegmüller, Micol Murtas

Cytology is an option for triaging human papillomavirus (HPV)-positive women. The interpretation of cytologic slides requires expertise and financial resources that are not always available in resource-limited settings. A solution could be offered by manua ...

2023

ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification

Jean-Philippe Thiran, Thomas Grégoire Stegmüller, Antoine Jérôme Spahr, Seyedbehzad Bozorgtabar

Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instanc ...

IEEE COMPUTER SOC2023

Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime

Jean-Philippe Thiran, Christian Robert Abbet, Thomas Grégoire Stegmüller, Seyedbehzad Bozorgtabar

Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its widespread implementation in low-resource settings. Deep learning-assisted telecy ...

Pergamon-Elsevier Science Ltd2023