research
∙
04/09/2023
On the dice loss gradient and the ways to mimic it
In the past few years, in the context of fully-supervised semantic segme...
share
research
∙
08/06/2021
Source-Free Domain Adaptation for Image Segmentation
Domain adaptation (DA) has drawn high interest for its capacity to adapt...
research
∙
05/03/2021
Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!
Standard losses for training deep segmentation networks could be seen as...
research
∙
12/11/2020
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
Few-shot segmentation has recently attracted substantial interest, with ...
research
∙
05/07/2020
Source-Relaxed Domain Adaptation for Image Segmentation
Domain adaptation (DA) has drawn high interests for its capacity to adap...
research
∙
08/15/2019
Discretely-constrained deep network for weakly supervised segmentation
An efficient strategy for weakly-supervised segmentation is to impose co...
research
∙
08/08/2019
Constrained domain adaptation for segmentation
We propose to adapt segmentation networks with a constrained formulation...
research
∙
04/10/2019
Curriculum semi-supervised segmentation
This study investigates a curriculum-style strategy for semi-supervised ...
research
∙
04/08/2019
Log-barrier constrained CNNs
This study investigates imposing inequality constraints on the outputs o...
research
∙
12/17/2018
Boundary loss for highly unbalanced segmentation
Widely used loss functions for convolutional neural network (CNN) segmen...
research
∙
05/12/2018