  Prototype learning is extensively used for few-shot segmentation. Typically,
a single prototype is obtained from the support feature by averaging the global
object information. However, using one prototype to represent all the
information may lead to ambiguities. In this paper, we propose two novel
modules, named superpixel-guided clustering (SGC) and guided prototype
allocation (GPA), for multiple prototype extraction and allocation.
Specifically, SGC is a parameter-free and training-free approach, which
extracts more representative prototypes by aggregating similar feature vectors,
while GPA is able to select matched prototypes to provide more accurate
guidance. By integrating the SGC and GPA together, we propose the Adaptive
Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to
object scale and shape variation. In addition, our network can easily
generalize to k-shot segmentation with substantial improvement and no
additional computational cost. In particular, our evaluations on COCO
demonstrate that ASGNet surpasses the state-of-the-art method by 5% in 5-shot
segmentation.
