This paper addresses the unrealistic aspect of the commonly adopted
Continuous Incremental Semantic Segmentation (CISS) scenario, termed
overlapped. We point out that overlapped allows the same image to reappear in
future tasks with different pixel labels, which is far from practical
incremental learning scenarios. Moreover, we identified that this flawed
scenario may lead to biased results for two commonly used techniques in CISS,
pseudo-labeling and exemplar memory, resulting in unintended advantages or
disadvantages for certain techniques. To mitigate this, a practical scenario
called partitioned is proposed, in which the dataset is first divided into
distinct subsets representing each class, and then the subsets are assigned to
each corresponding task. This efficiently addresses the issue above while
meeting the requirement of CISS scenario, such as capturing the background
shifts. Furthermore, we identify and address the code implementation issues
related to retrieving data from the exemplar memory, which was ignored in
previous works. Lastly, we introduce a simple yet competitive memory-based
baseline, MiB-AugM, that handles background shifts of current tasks in the
exemplar memory. This baseline achieves state-of-the-art results across
multiple tasks involving learning numerous new classes.

本文针对常用的连续增量语义分割（Continuous Incremental Semantic Segmentation，CISS）场景中的不切实际之处进行了研究，提出了一个实用的场景，即分割（partitioned），该场景能够解决 CISS 中的偏差问题，并通过引入一种名为 MiB-AugM 的基于记忆机制的基准模型，实现了多任务学习与学习新类的最新结果。