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Semantic segmentation, vital for applications ranging from autonomous drivingto robotics, faces significant challenges in domains where collecting largeannotated datasets is difficult or prohibitively expensive. In such contexts,such as medicine and agriculture, the scarcity of training images hampersprogress.  Introducing Few-Shot Semantic Segmentation, a novel task in computer vision,which aims at designing models capable of segmenting new semantic classes withonly a few examples. This paper consists of a comprehensive survey of Few-ShotSemantic Segmentation, tracing its evolution and exploring various modeldesigns, from the more popular conditional and prototypical networks to themore niche latent space optimization methods, presenting also the newopportunities offered by recent foundational models. Through a chronologicalnarrative, we dissect influential trends and methodologies, providing insightsinto their strengths and limitations. A temporal timeline offers a visualroadmap, marking key milestones in the field's progression.  Complemented by quantitative analyses on benchmark datasets and qualitativeshowcases of seminal works, this survey equips readers with a deepunderstanding of the topic. By elucidating current challenges, state-of-the-artmodels, and prospects, we aid researchers and practitioners in navigating theintricacies of Few-Shot Semantic Segmentation and provide ground for futuredevelopment.

Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges