Deep Learning Based on Attention in Semantic Segmentation: An Introductory Survey

Abstract

Semantic segmentation refers to labeling each pixel in the scene to its belonging object. It is a critical task for many computer vision applications that requires scene understanding because It attempts to mimic human perceptual grouping. Despite the unremitting efforts in this field, it is still a challenge and preoccupies of researchers. Semantic segmentation performance improved using deep learning rather than traditional methods. Semantic segmentation based on deep learning models requires capturing local and global context information, where deep learning models usually can extract one of them but is challenging to integrate between them. Deep learning based on attention mechanisms can gather between the capturing of local and glopal information, so it is increasingly employed in semantic segmentation. This paper gives an introductory survey of the rising topic attention mechanisms in semantic segmentation. At first, it will discuss the concept of attention and its integration with semantic segmentation requirements. Then, it will review deep learning based on attention mechanisms in semantic segmentation. Index Terms— attention concept, computer vision, deep learning, semantic segmentation.