Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges
arxiv(2023)
摘要
Semantic segmentation, vital for applications ranging from autonomous driving
to robotics, faces significant challenges in domains where collecting large
annotated datasets is difficult or prohibitively expensive. In such contexts,
such as medicine and agriculture, the scarcity of training images hampers
progress.
Introducing Few-Shot Semantic Segmentation, a novel task in computer vision,
which aims at designing models capable of segmenting new semantic classes with
only a few examples. This paper consists of a comprehensive survey of Few-Shot
Semantic Segmentation, tracing its evolution and exploring various model
designs, from the more popular conditional and prototypical networks to the
more niche latent space optimization methods, presenting also the new
opportunities offered by recent foundational models. Through a chronological
narrative, we dissect influential trends and methodologies, providing insights
into their strengths and limitations. A temporal timeline offers a visual
roadmap, marking key milestones in the field's progression.
Complemented by quantitative analyses on benchmark datasets and qualitative
showcases of seminal works, this survey equips readers with a deep
understanding of the topic. By elucidating current challenges, state-of-the-art
models, and prospects, we aid researchers and practitioners in navigating the
intricacies of Few-Shot Semantic Segmentation and provide ground for future
development.
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