Anderson Brilhador 
, André Eugênio Lazzaretti , & Heitor Silvério Lopes
Abstract: This article provides a survey of open-world learning in the context of image segmentation and a subset of associated tasks that exhibit desirable characteristics for real-world applications, such as autonomous driving, industrial inspection, medical diagnosis, remote sensing, among others. The objective is to identify the main approaches, challenges, and gaps in this research field. Through a rigorous procedure, 39 articles published between 2013 and 2023 were selected for analysis. Then, a review was conducted to examine these documents, and three main research questions were posed. After an extensive analysis, results indicate that open-world learning for image segmentation has been explored only recently, and it seems to be a promising field of research for the upcoming years. Established on this reviewed literature, we provide the potential directions and open gaps for future works on this topic.
Keywords: Open-world, open-set, incremental learning, image segmentation, review, survey
DOI code: 10.21528/lnlm-vol23-no1-art3
PDF file: vol23-no1-art3.pdf
BibTex file: vol23-no1-art3.bib