A systematic review on open-set segmentation.

Open-set semantic segmentation remains yet a challenging task, not only due to the inherent challenges of pixel-wise classification but also the precise segmentation of categories not seen during training. The pursuit of that task is rapidly growing in the Computer Vision community, urging the need to organize the literature. In this paper, we extend our previous work by conducting a more comprehensive systematic mapping of the open-set segmentation literature between January 2001 and January 2023 and proposing a novel taxonomy. Our goal is to provide a broad understanding of current trends for the open-set semantic segmentation (OSS) task defined by existing approaches that may influence future methods. By characterizing methodologies in terms of open-set identification strategies, data inputs, and other relevant aspects, we present a structured view of how researchers are advancing in the field of open-set semantic segmentation. To the best of the authors' knowledge, this is the first systematic review of OSS methods. Moreover, we apply the proposed taxonomy to selected methods for open-set recognition, outlining important similarities and differences of such a closely related field. [Display omitted] • Systematic review of papers related to open-set semantic segmentation for the past 20 years. • The proposed taxonomy aims to organize the literature on open-set segmentation. • Seminal papers on open-set recognition are classified under the proposed taxonomy. • Applications like autonomous driving and remote sensing were found to commonly resort to the open set strategy. • Methods tackling open-world are becoming more commom; [ABSTRACT FROM AUTHOR]