GAPS: Few-Shot Incremental Semantic Segmentation via Guided Copy-Paste Synthesis

Ri-Zhao Qiu, Peiyi Chen, Wangzhe Sun, Yu-Xiong Wang, Kris Hauser

Keywords: continual learning, incremental learning, incremental segmentation, few-shot learning

Abstract: Few-shot incremental segmentation is the task of updating a segmentation model, as novel classes are introduced online overtime with a small number of training images. Although incremental segmentation methods exist in the literature, they tend to fall short in the few-shot regime and when given partially-annotated training images, where only the novel class is segmented. This paper proposes a data synthesizer, Guided copy-And-Paste Synthesis (GAPS), that improves the performance of few-shot incremental segmentation in a model-agnostic fashion. Despite the great success of copy-paste synthesis in the conventional offline visual recognition, we demonstrate substantially degraded performance of its naive extension in our online scenario, due to newly encountered challenges. To this end, GAPS (i) addresses the partial-annotation problem by leveraging copy-paste to generate fully-labeled data for training, (ii) helps augment the few images of novel objects by introducing a guided sampling process, and (iii) mitigates catastrophic forgetting by employing a diverse memory-replay buffer. Compared to existing state-of-the-art methods, GAPS dramatically boosts the novel IoU of baseline methods on established few-shot incremental segmentation benchmarks by up to 80%. More notably, GAPS maintains good performance in even more impoverished annotation settings, where only single instances of novel objects are annotated.

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TL;DR: This paper proposes a guided copy-paste synthesis process for few-shot incremental semantic segmentation, which can be combined with existing methods to achieve dramatic performance increase.