Few-Shot Segmentation (FSS) is challenging for limited support images and
large intra-class appearance discrepancies. Due to the huge difference between
support and query samples, most existing approaches focus on extracting
high-level representations of the same layers for support-query correlations
but neglect the shift issue between different layers and scales. In this paper,
we propose a Multi-Context Interaction Network (MCINet) to remedy this issue by
fully exploiting and interacting with the multi-scale contextual information
contained in the support-query pairs. Specifically, MCINet improves FSS from
the perspectives of boosting the query representations by incorporating the
low-level structural information from another query branch into the high-level
semantic features, enhancing the support-query correlations by exploiting both
the same-layer and adjacent-layer features, and refining the predicted results
by a multi-scale mask prediction strategy, with which the different scale
contents have bidirectionally interacted. Experiments on two benchmarks
demonstrate that our approach reaches SOTA performances and outperforms the
best competitors with many desirable advantages, especially on the challenging
COCO dataset