13 citations · 23 across the 6 of their papers we have counts for
7 papers · 1 filter
A patch-based architecture for multi-label classification from single label annotations
Warren Jouanneau, Aurélie Bugeau, Marc Palyart +2
In this paper, we propose a patch-based architecture for multi-label classification problems where only a single positive label is observed in images of the dataset. Our contributi…
POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring
Reda Abdellah Kamraoui, Vinh-Thong Ta, Nicolas Papadakis +3
Semi-supervised learning (SSL) uses unlabeled data to compensate for the scarcity of annotated images and the lack of method generalization to unseen domains, two usual problems in…
Multi-task deep learning for image segmentation using recursive approximation tasks
Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis +3
Fully supervised deep neural networks for segmentation usually require a massive amount of pixel-level labels which are manually expensive to create. In this work, we develop a mul…
Variational Osmosis for Non-linear Image Fusion
Simone Parisotto, Luca Calatroni, Aurélie Bugeau +2
We propose a new variational model for non-linear image fusion. Our approach is based on the use of an osmosis energy term related to the one studied in Vogel et al. (2013) and Wei…
Learning to segment microscopy images with lazy labels
Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis +2
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instan…
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
Philip Sellars, Angelica Aviles-Rivero, Nicolas Papadakis +3
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification. We first introduce a novel superpixel algorithm based on the spectral cova…