  Few-shot segmentation (FSS) aims to segment objects of new categories given
only a handful of annotated samples. Previous works focus their efforts on
exploring the support information while paying less attention to the mining of
the critical query branch. In this paper, we rethink the importance of support
information and propose a new query-centric FSS model Adversarial Mining
Transformer (AMFormer), which achieves accurate query image segmentation with
only rough support guidance or even weak support labels. The proposed AMFormer
enjoys several merits. First, we design an object mining transformer (G) that
can achieve the expansion of incomplete region activated by support clue, and a
detail mining transformer (D) to discriminate the detailed local difference
between the expanded mask and the ground truth. Second, we propose to train G
and D via an adversarial process, where G is optimized to generate more
accurate masks approaching ground truth to fool D. We conduct extensive
experiments on commonly used Pascal-5i and COCO-20i benchmarks and achieve
state-of-the-art results across all settings. In addition, the decent
performance with weak support labels in our query-centric paradigm may inspire
the development of more general FSS models. Code will be available at
https://github.com/Wyxdm/AMNet.
