A crucial module of the widely applied machine learning (ML) model is the model training
phase, which involves large-scale training data, often including sensitive private data. ML …

Sparsely annotated semantic segmentation (SASS) aims to train a segmentation network
with coarse-grained (ie, point-, scribble-, and block-wise) supervisions, where only a small …

Sparsely annotated semantic segmentation (SASS) aims to learn a segmentation model by
images with sparse labels (ie, points or scribbles). Existing methods mainly focus on …

Achieving fusion of deep learning with combinatorial algorithms promises transformative
changes to artificial intelligence. One possible approach is to introduce combinatorial …

Achieving fusion of deep learning with combinatorial algorithms promises transformative
changes to artificial intelligence. One possible approach is to introduce combinatorial …

OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …

We propose a novel weakly supervised learning segmentation based on several global
constraints derived from box annotations. Particularly, we leverage a classical tightness prior …

Many automated processes such as auto-piloting rely on a good semantic segmentation as
a critical component. To speed up performance, it is common to downsample the input …

State-of-the-art approaches for semantic segmentation rely on deep convolutional neural
networks trained on fully annotated datasets, that have been shown to be notoriously …

Weakly-supervised segmentation with label-efficient sparse annotations has attracted
increasing research attention to reduce the cost of laborious pixel-wise labeling process …