Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

Image segmentation is often ambiguous at the level of individual image patches and
requires contextual information to reach label consensus. In this paper we introduce …

Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …

In recent years, deep learning-based networks have achieved state-of-the-art performance
in medical image segmentation. Among the existing networks, U-Net has been successfully …

Spatial pyramid pooling module or encode-decoder structure are used in deep neural
networks for semantic segmentation task. The former networks are able to encode multi …

While self-training has advanced semi-supervised semantic segmentation, it severely suffers
from the long-tailed class distribution on real-world semantic segmentation datasets that …

Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …

Abstract The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset
containing 25,000 high-resolution images annotated into 66 object categories with …

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …