Deep neural networks (DNNs) for the semantic segmentation of images are usually trained
to operate on a predefined closed set of object classes. This is in contrast to the"" open …

State-of-the-art methods for semantic segmentation are based on deep neural networks that
are known to be data-hungry. Region-based active learning has shown to be a promising …

Using deep learning, we now have the ability to create exceptionally good semantic
segmentation systems; however, collecting the prerequisite pixel-wise annotations for …

The expensive annotation cost is notoriously known as the main constraint for the
development of the point cloud semantic segmentation technique. Active learning methods …

In this work, we for the first time present a method for detecting labeling errors in image
datasets with semantic segmentation, ie, pixel-wise class labels. Annotation acquisition for …

Uncertainty estimation is an essential and heavily-studied component for the reliable
application of semantic segmentation methods. While various studies exist claiming …

Deep learning models are the state-of-the-art methods for semantic point cloud
segmentation, the success of which relies on the availability of large-scale annotated …

This paper proposes a new active learning method for semantic segmentation. The core of
our method lies in a new annotation query design. It samples informative local image …

Learning-based solutions for vision tasks require a large amount of labeled training data to
ensure their performance and reliability. In single-task vision-based settings, inconsistency …

Learning semantic segmentation requires pixel-wise annotations, which can be time-
consuming and expensive. To reduce the annotation cost, we propose a superpixel-based …