The Few-Shot Segmentation (FSS) aims to accomplish the novel class segmentation task with a few annotated images. Current FSS research based on meta-learning focus on designing a complex interaction mechanism between the query and support feature. However, unlike humans who can rapidly learn new things from limited samples, the existing approach relies solely on fixed feature matching to tackle new tasks, lacking adaptability. In this paper, we propose a novel framework based on the adapter mechanism, namely Adaptive FSS, which can efficiently adapt the existing FSS model to the novel classes. In detail, we design the Prototype Adaptive Module (PAM), which utilizes accurate category information provided by the support set to derive class prototypes, enhancing class-specific information in the multi-stage representation. In addition, our approach is compatible with in diverse FSS methods with different backbones by simply inserting PAM between the layers of the encoder. Experiments demonstrate that our method effectively improves the performance of the FSS models (e.g., MSANet, HDMNet, FPTrans, and DCAMA) and achieve new state-of-the-art (SOTA) results (i.e., 72.4\% and 79.1\% mIoU on PASCAL-5$^i$ 1-shot and 5-shot settings, 52.7\% and 60.0\% mIoU on COCO-20$^i$ 1-shot and 5-shot settings). Our code can be available at https://github.com/jingw193/AdaptiveFSS.

翻译：少样本分割（Few-Shot Segmentation, FSS）旨在利用少量标注图像完成新类别的分割任务。当前基于元学习的FSS研究主要聚焦于设计查询集与支持集特征之间的复杂交互机制。然而，与人类能从有限样本中快速学习新事物的能力不同，现有方法仅依赖固定特征匹配来处理新任务，缺乏适应性。本文提出一种基于适配器机制的新型框架——Adaptive FSS，该框架能够高效地将现有FSS模型适应于新类别。具体而言，我们设计了原型自适应模块（Prototype Adaptive Module, PAM），该模块利用支持集提供的精确类别信息推导类别原型，从而增强多阶段表征中的类别特异性信息。此外，通过简单地将PAM嵌入编码器各层之间，我们的方法可兼容采用不同骨干网络的多种FSS方法。实验表明，本方法能有效提升FSS模型（如MSANet、HDMNet、FPTrans及DCAMA）的性能，并在PASCAL-5$^i$的1-shot与5-shot设置下分别达到72.4%和79.1% mIoU，在COCO-20$^i$的1-shot与5-shot设置下分别达到52.7%和60.0% mIoU，创下新的最优结果（State-of-the-Art, SOTA）。我们的代码已开源至https://github.com/jingw193/AdaptiveFSS。