Segment Anything: A Review

Abstract

Segment Anything (SA) is a state-of-the art method for universal object segmentation, which does not need task-specific training. Herein, we emphasize that SA can overcome the limitations of traditional segmentation frameworks based on requiring extensive manually annotated datasets and predefined architectures, as extensively documented in this review. SB supercharges performance and reduces cost by combining Mutual Information learning with an Efficient Transformer architecture, benefiting from a substantially larger pool of in-the-wild data. In this paper we review SA and its specific key innovations generality, resource boundedness, and scalability to large datasets. We also face obstacles such as data biases, computational complexity, real-world application issues and consider security as well as privacy in federated learning scenarios. It discusses areas for future research, such as increasing precision and robustness, incorporating the federated learning aspect and concerns regarding its ethical use in high risk domains of application. In this review, we highlight the transformative capacity that SA may bring to volume-wise object segmentation and urge the community to leverage on top of these new venues for a breakthrough in AI-vision systems.