Continual Segmentation with Disentangled Objectness Learning and Class Recognition
This addresses the problem of catastrophic forgetting in continual segmentation for computer vision applications, representing an incremental improvement over prior methods.
The paper tackles the challenge of continual segmentation by proposing CoMasTRe, which disentangles the task into objectness learning and classification stages, outperforming existing per-pixel and query-based methods on PASCAL VOC and ADE20K datasets.
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.