Continual Segmentation with Disentangled Objectness Learning and Class Recognition

• URL: http://arxiv.org/abs/2403.03477v3
• Date: Mon, 1 Apr 2024 03:35:25 GMT
• Title: Continual Segmentation with Disentangled Objectness Learning and Class Recognition
• Authors: Yizheng Gong, Siyue Yu, Xiaoyang Wang, Jimin Xiao,
• Abstract summary: We propose CoMasTRe to 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. To further mitigate the forgetting of old classes, we design a multi-label class distillation strategy suited for segmentation.
• Score: 19.23268063605072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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