  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 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.
