  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.
