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
this https URL

支持信息和查询中心 FSS 模型的对抗挖掘变压器 (AMFormer) 提供了准确的查询图像分割，即使只有粗略的支持指导或弱支持标签，通过优化产生更接近于地面真实的精确掩码来愚弄详细挖掘变压器

聚焦查询：用于少样本分割的对抗性挖掘 Transformer

Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation

We tackle the challenging task of few-shot segmentation in this work. It is
essential for few-shot semantic segmentation to fully utilize the support
information. Previous methods typically adopt masked average pooling over the
support feature to extract the support clues as a global vector, usually
dominated by the salient part and lost certain essential clues. In this work,
we argue that every support pixel's information is desired to be transferred to
all query pixels and propose a Correspondence Matching Network (CMNet) with an
Optimal Transport Matching module to mine out the correspondence between the
query and support images. Besides, it is critical to fully utilize both local
and global information from the annotated support images. To this end, we
propose a Message Flow module to propagate the message along the inner-flow
inside the same image and cross-flow between support and query images, which
greatly helps enhance the local feature representations. Experiments on PASCAL
VOC 2012, MS COCO, and FSS-1000 datasets show that our network achieves new
state-of-the-art few-shot segmentation performance.

本文提出了一种支持信息相互利用、弥补之前模型的不足之处的少样本分割算法。通过建立图像间的对应关系和信息传播机制，实现对支持图像的多方面信息的全面利用，达到了最新的相对优良的分割效果。