  Few-shot segmentation, which aims to segment unseen-class objects given only
a handful of densely labeled samples, has received widespread attention from
the community. Existing approaches typically follow the prototype learning
paradigm to perform meta-inference, which fails to fully exploit the underlying
information from support image-mask pairs, resulting in various segmentation
failures, e.g., incomplete objects, ambiguous boundaries, and distractor
activation. To this end, we propose a simple yet versatile framework in the
spirit of divide-and-conquer. Specifically, a novel self-reasoning scheme is
first implemented on the annotated support image, and then the coarse
segmentation mask is divided into multiple regions with different properties.
Leveraging effective masked average pooling operations, a series of
support-induced proxies are thus derived, each playing a specific role in
conquering the above challenges. Moreover, we devise a unique parallel decoder
structure that integrates proxies with similar attributes to boost the
discrimination power. Our proposed approach, named divide-and-conquer proxies
(DCP), allows for the development of appropriate and reliable information as a
guide at the "episode" level, not just about the object cues themselves.
Extensive experiments on PASCAL-5i and COCO-20i demonstrate the superiority of
DCP over conventional prototype-based approaches (up to 5~10% on average),
which also establishes a new state-of-the-art. Code is available at
github.com/chunbolang/DCP.
