Toward Generative Understanding: Incremental Few-Shot Semantic Segmentation With Diffusion Models

Incremental Few-shot Semantic Segmentation (iFSS) aims to learn novel classes with limited samples while preserving segmentation capability for base classes, addressing the challenge of continual learning of novel classes and catastrophic forgetting of previously seen classes. Existing methods mainly rely on techniques such as knowledge distillation and background learning, which, while partially effective, still suffer from issues such as feature drift and limited generalization to real-world novel classes, primarily due to a bidirectional coupling bottleneck between the learning of base classes and novel classes. To address these challenges, we propose, for the first time, a diffusion-based generative framework for iFSS. Specifically, we bridge the gap between generative and discriminative tasks through an innovative binary-to-RGB mask mapping mechanism, enabling pre-trained diffusion models to focus on target regions via class-specific semantic embedding optimization while sharpening foreground-background contrast with color embeddings. A lightweight post-processor then refines the generated images into high-quality binary masks. Crucially, by leveraging diffusion priors, our framework avoids complex training strategies. The optimization of class-specific semantic embeddings decouples the embedding spaces of base and novel classes, inherently preventing feature drift, mitigating catastrophic forgetting, and enabling rapid novel-class adaptation. Experimental results show that our method achieves state-of-the-art performance on the PASCAL- $5^{i}$ and COCO- $20^{i}$ datasets using much less data than other methods, and exhibiting competitive results in cross-domain few-shot segmentation tasks. Project page: https://ifss-diff.github.io/.