Most continual segmentation methods tackle the problem as a per-pixel classification task. However, such a paradigm is very challenging, and we find query-based segmenters with built-in objectness have inherent advantages compared with per-pixel ones, as objectness has strong transfer ability and forgetting resistance. Based on these findings, we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classification. CoMasTRe uses a two-stage segmenter learning class-agnostic mask proposals at the first stage and leaving recognition to the second stage. During continual learning, a simple but effective distillation is adopted to strengthen objectness. To further mitigate the forgetting of old classes, we design a multi-label class distillation strategy suited for segmentation. We assess the effectiveness of CoMasTRe on PASCAL VOC and ADE20K. Extensive experiments show that our method outperforms per-pixel and query-based methods on both datasets. Code will be available at https://github.com/jordangong/CoMasTRe.

基于强大的目标性和防遗忘特性，我们提出了CoMasTRe方法，它将持续分割任务分为两个阶段，通过学习抗遗忘的目标性特征和经过良好研究的分类特征，采用简单但有效的蒸馏策略来强化目标性，并通过多标签类蒸馏策略减轻旧类别的遗忘，实验证明该方法在PASCAL VOC数据集上优于像素级和基于查询的方法。

具有分离的客体性学习和类别识别的持续分割