Survey of semantic image segmentation over two decades
Survey of semantic image segmentation over two decades
Semantic Image Segmentation: Two Decades of Research
arXiv paper abstract https://arxiv.org/abs/2302.06378
arXiv PDF paper https://arxiv.org/pdf/2302.06378.pdf
Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image.
This survey is an effort to summarize two decades of research in the field of SiS, where ... propose a literature review of solutions starting from early historical methods followed by an overview of more recent deep learning methods including the latest trend of using transformers.
... complement the review by discussing particular cases of the weak supervision and side machine learning techniques that can be used to improve the semantic segmentation such as curriculum, incremental or self-supervised learning.
... Since unlabeled data is instead significantly cheaper to obtain, it is not surprising that Unsupervised Domain Adaptation (UDA) reached a broad success within the semantic segmentation community.
Therefore, a second core contribution of this book is to summarize five years of a rapidly growing field, Domain Adaptation for Semantic Image Segmentation (DASiS) which embraces the importance of semantic segmentation itself and a critical need of adapting segmentation models to new environments.
... unveil also newer trends such as multi-domain learning, domain generalization, domain incremental learning, test-time adaptation and source-free domain adaptation ...
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