Search Results for author: Thomas Stegmüller

Found 5 papers, 4 papers with code

CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping

1 code implementation11 Oct 2023 Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar, Jean-Philippe Thiran, Tinne Tuytelaars

Leveraging nearest neighbor retrieval for self-supervised representation learning has proven beneficial with object-centric images.

In-Context Learning Object +3

Adaptive Similarity Bootstrapping for Self-Distillation based Representation Learning

1 code implementation ICCV 2023 Tim Lebailly, Thomas Stegmüller, Behzad Bozorgtabar, Jean-Philippe Thiran, Tinne Tuytelaars

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.

Contrastive Learning Representation Learning

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

2 code implementations CVPR 2023 Thomas Stegmüller, Tim Lebailly, Behzad Bozorgtabar, Tinne Tuytelaars, Jean-Philippe Thiran

More importantly, the clustering algorithm conjointly operates on the features of both views, thereby elegantly bypassing the issue of content not represented in both views and the ambiguous matching of objects from one crop to the other.

Clustering Online Clustering +5

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

1 code implementation15 Feb 2022 Thomas Stegmüller, Behzad Bozorgtabar, Antoine Spahr, Jean-Philippe Thiran

We further introduce a novel mixing data-augmentation, namely ScoreMix, by leveraging the image's semantic distribution to guide the data mixing and produce coherent sample-label pairs.

Data Augmentation Domain Generalization +3

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