Multi-Content Interaction Network for Few-Shot Segmentation

Few-Shot Segmentation (FSS) poses significant challenges due to limited support images and large intraclass appearance discrepancies. Most existing approaches focus on aligning the support-query correlations from the same layer of the frozen backbone while neglecting the bias between different tasks and different layers. In this article, we propose a Multi-Content Interaction Network (MCINet) to remedy these issues by fully exploiting and interacting with the different contextual information contained in distinct branches. Specifically, MCINet improves FSS from three perspectives: (1) boosting the query representations through incorporating the independent information from another learnable branch into the features from the frozen backbone, (2) enhancing the support-query correlations by exploiting both the same-layer and adjacent-layer features, and (3) refining the predicted results with a multi-scale mask prediction strategy. Experiments on three benchmarks demonstrate that our approach reaches state-of-the-art performances and outperforms the best competitors with many desirable advantages, especially on the challenging COCO dataset. Code will be released on GitHub (https://github.com/chenhao-zju/mcinet).