A Brief Survey on Semantic Segmentation with Deep Learning

By Shijie Hao, Yuanen Zhou, Yanrong Guo. Neurocomputing, 2020

583 citations recorded by the source corpus.

Abstract 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 techniques. A large number of novel methods have been proposed. This paper aims to provide a brief review of research efforts on deep-learning-based semantic segmentation methods. We categorize the related research according to its supervision level, i.e., fully-supervised methods, weakly-supervised methods and semi-supervised methods. We also discuss the common challenges of the current research, and present several valuable growing research points in this field. This survey is expected to familiarize readers with the progress and challenges of semantic segmentation research in the deep learning era.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d6dfa721a2982927da0c43abbd0c39daa0a9904a

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In Computer Vision and Pattern Recognition

Citations in the corpus

Transformer-Based Visual Segmentation: A Survey (2023, background): “Meanwhile, there are previous surveys on the deep-learning-based segmentation [37], [38], [39].”