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 …

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 multi-class video segmentation is one of the most challenging yet least
studied research problems in computer vision. This study aims to investigate two main …

Training deep neural networks with only a few labeled samples can lead to overfitting. This
is problematic in semi-supervised learning where only a few labeled samples are available …

For fine-scale prediction tasks such as semantic segmentation, existing segmentation
models cannot support detailed segmentation due to the difficulty of assigning deep feature …

The article presents the concepts of control systems that use methods of automatic analysis
and interpretation of signals. These issues are presented through the description of three …

Scene text detection has been gaining a lots of focus in research. Even though the recent
methods are able to detect text in complex background having complex shapes with a fairly …