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Few-shot segmentation focuses on the generalization of models to segmentunseen object instances with limited training samples. Although tremendousimprovements have been achieved, existing methods are still constrained by twofactors. (1) The information interaction between query and support images isnot adequate, leaving intra-class gap. (2) The object categories at thetraining and inference stages have no overlap, leaving the inter-class gap.Thus, we propose a framework, BriNet, to bridge these gaps. First, moreinformation interactions are encouraged between the extracted features of thequery and support images, i.e., using an Information Exchange Module toemphasize the common objects. Furthermore, to precisely localize the queryobjects, we design a multi-path fine-grained strategy which is able to makebetter use of the support feature representations. Second, a new onlinerefinement strategy is proposed to help the trained model adapt to unseenclasses, achieved by switching the roles of the query and the support images atthe inference stage. The effectiveness of our framework is demonstrated byexperimental results, which outperforms other competitive methods and leads toa new state-of-the-art on both PASCAL VOC and MSCOCO dataset.

BriNet: Towards Bridging the Intra-class and Inter-class Gaps in One-Shot Segmentation