activity
20102023
most citedSpread spectrum magnetic resonance imaging

99 citations · 132 across the 10 of their papers we have counts for

collaborators

12 papers

cs.CV2024

PIV3CAMS: a multi-camera dataset for multiple computer vision problems and its application to novel view-point synthesis

Sohyeong Kim, Martin Danelljan, Radu Timofte +2

The modern approaches for computer vision tasks significantly rely on machine learning, which requires a large number of quality images. While there is a plethora of image datasets…

cs.CV2024

Un-Mixing Test-Time Normalization Statistics: Combatting Label Temporal Correlation

Devavrat Tomar, Guillaume Vray, Jean-Philippe Thiran +1

Recent test-time adaptation methods heavily rely on nuanced adjustments of batch normalization (BN) parameters. However, one critical assumption often goes overlooked: that of inde…

cs.CV2023

AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays

Behzad Bozorgtabar, Dwarikanath Mahapatra, Jean-Philippe Thiran

Unsupervised anomaly detection in medical images such as chest radiographs is stepping into the spotlight as it mitigates the scarcity of the labor-intensive and costly expert anno…

cs.CE2023

CACTUS: A Computational Framework for Generating Realistic White Matter Microstructure Substrates

Juan Luis Villarreal-Haro, Remy Gardier, Erick J Canales-Rodriguez +4

Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in c…

cs.CV20236 cited

Neural Implicit Dense Semantic SLAM

Yasaman Haghighi, Suryansh Kumar, Jean-Philippe Thiran +1

Visual Simultaneous Localization and Mapping (vSLAM) is a widely used technique in robotics and computer vision that enables a robot to create a map of an unfamiliar environment us…

cs.CV2023

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

Thomas Stegmüller, Tim Lebailly, Behzad Bozorgtabar +2

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…