Existing few-shot segmentation methods have achieved great progress based on
the support-query matching framework. But they still heavily suffer from the
limited coverage of intra-class variations from the few-shot supports provided.
Motivated by the simple Gestalt principle that pixels belonging to the same
object are more similar than those to different objects of same class, we
propose a novel self-support matching strategy to alleviate this problem, which
uses query prototypes to match query features, where the query prototypes are
collected from high-confidence query predictions. This strategy can effectively
capture the consistent underlying characteristics of the query objects, and
thus fittingly match query features. We also propose an adaptive self-support
background prototype generation module and self-support loss to further
facilitate the self-support matching procedure. Our self-support network
substantially improves the prototype quality, benefits more improvement from
stronger backbones and more supports, and achieves SOTA on multiple datasets.
Codes are at https://github.com/fanq15/SSP.

利用自支持匹配策略提高 few-shot 语义分割精度，通过适应性自支持背景原型生成模块和自支持损失进一步促进自支持匹配过程，该策略有效地捕获查询对象的一致潜在特征，从而更适当地匹配查询特征，其实现了多个数据集上的 SOTA，源码可在 https://github.com/fanq15/SSP 找到。