Continual learning, also known as incremental learning or life-long learning,
stands at the forefront of deep learning and AI systems. It breaks through the
obstacle of one-way training on close sets and enables continuous adaptive
learning on open-set conditions. In the recent decade, continual learning has
been explored and applied in multiple fields especially in computer vision
covering classification, detection and segmentation tasks. Continual semantic
segmentation (CSS), of which the dense prediction peculiarity makes it a
challenging, intricate and burgeoning task. In this paper, we present a review
of CSS, committing to building a comprehensive survey on problem formulations,
primary challenges, universal datasets, neoteric theories and multifarious
applications. Concretely, we begin by elucidating the problem definitions and
primary challenges. Based on an in-depth investigation of relevant approaches,
we sort out and categorize current CSS models into two main branches including
\textit{data-replay} and \textit{data-free} sets. In each branch, the
corresponding approaches are similarity-based clustered and thoroughly
analyzed, following qualitative comparison and quantitative reproductions on
relevant datasets. Besides, we also introduce four CSS specialities with
diverse application scenarios and development tendencies. Furthermore, we
develop a benchmark for CSS encompassing representative references, evaluation
results and reproductions, which is available
at~https://github.com/YBIO/SurveyCSS. We hope this survey can serve as a
reference-worthy and stimulating contribution to the advancement of the
life-long learning field, while also providing valuable perspectives for
related fields.

连续学习在深度学习和人工智能系统中具有突破性意义，本文以连续语义分割为例，全面调查其问题形式、主要挑战、通用数据集、新理论和多样应用，并分类整理了当前 CSS 模型中的数据回放和无数据集两个主要分支方法，并提供了具有不同应用场景和发展趋势的四个 CSS 特性。