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A survey on label-efficient deep image segmentation: Bridging the gap between weak supervision and dense prediction
The rapid development of deep learning has made a great progress in image segmentation,
one of the fundamental tasks of computer vision. However, the current segmentation …
one of the fundamental tasks of computer vision. However, the current segmentation …
Semi-supervised semantic segmentation with error localization network
This paper studies semi-supervised learning of semantic segmentation, which assumes that
only a small portion of training images are labeled and the others remain unlabeled. The …
only a small portion of training images are labeled and the others remain unlabeled. The …
Corrmatch: Label propagation via correlation matching for semi-supervised semantic segmentation
This paper presents a simple but performant semi-supervised semantic segmentation
approach called CorrMatch. Previous approaches mostly employ complicated training …
approach called CorrMatch. Previous approaches mostly employ complicated training …
PRCL: Probabilistic representation contrastive learning for semi-supervised semantic segmentation
Tremendous breakthroughs have been developed in Semi-Supervised Semantic
Segmentation (S4) through contrastive learning. However, due to limited annotations, the …
Segmentation (S4) through contrastive learning. However, due to limited annotations, the …
Semi-supervised semantic segmentation based on pseudo-labels: A survey
Semantic segmentation is an important and popular research area in computer vision that
focuses on classifying pixels in an image based on their semantics. However, supervised …
focuses on classifying pixels in an image based on their semantics. However, supervised …
A survey on semi-supervised semantic segmentation
Semantic segmentation is one of the most challenging tasks in computer vision. However, in
many applications, a frequent obstacle is the lack of labeled images, due to the high cost of …
many applications, a frequent obstacle is the lack of labeled images, due to the high cost of …
[HTML][HTML] Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation
Uncertainty quantification in automated image analysis is highly desired in many
applications. Typically, machine learning models in classification or segmentation are only …
applications. Typically, machine learning models in classification or segmentation are only …
Space engage: Collaborative space supervision for contrastive-based semi-supervised semantic segmentation
Abstract Semi-Supervised Semantic Segmentation (S4) aims to train a segmentation model
with limited labeled images and a substantial volume of unlabeled images. To improve the …
with limited labeled images and a substantial volume of unlabeled images. To improve the …
Diverse cotraining makes strong semi-supervised segmentor
Deep co-training has been introduced to semi-supervised segmentation and achieves
impressive results, yet few studies have explored the working mechanism behind it. In this …
impressive results, yet few studies have explored the working mechanism behind it. In this …
Multithreshold image segmentation technique using remora optimization algorithm for diabetic retinopathy detection from fundus images
VD Vinayaki, R Kalaiselvi - Neural Processing Letters, 2022 - Springer
One of the most common complications of diabetes mellitus is diabetic retinopathy (DR),
which produces lesions on the retina. A novel framework for DR detection and classification …
which produces lesions on the retina. A novel framework for DR detection and classification …