Few-shot segmentation focuses on the generalization of models to segment
unseen object instances with limited training samples. Although tremendous
improvements have been achieved, existing methods are still constrained by two
factors. (1) The information interaction between query and support images is
not adequate, leaving intra-class gap. (2) The object categories at the
training and inference stages have no overlap, leaving the inter-class gap.
Thus, we propose a framework, BriNet, to bridge these gaps. First, more
information interactions are encouraged between the extracted features of the
query and support images, i.e., using an Information Exchange Module to
emphasize the common objects. Furthermore, to precisely localize the query
objects, we design a multi-path fine-grained strategy which is able to make
better use of the support feature representations. Second, a new online
refinement strategy is proposed to help the trained model adapt to unseen
classes, achieved by switching the roles of the query and the support images at
the inference stage. The effectiveness of our framework is demonstrated by
experimental results, which outperforms other competitive methods and leads to
a new state-of-the-art on both PASCAL VOC and MSCOCO dataset.

提出了一种名为 BriNet 的分割框架，该框架应用信息交换模块和多路径细粒度策略来提高查询和支持图像之间的信息交互，进而实现精确定位查询对象；并且还提出了一种新的在线细化策略，帮助训练好的模型适应未见过的类别，并在 PASCAL VOC 和 MSCOCO 数据集上创造出新的最优效果。