Found missing semantics: Supplemental prototype network for few-shot semantic segmentation

Few-shot semantic segmentation alleviates the problem of massive data requirements and high costs in semantic segmentation tasks. By learning from support set, few-shot semantic segmentation can segment new classes. However, existing few-shot semantic segmentation methods suffer from information loss during the process of mask average pooling. To address this problem, we propose a supplemental prototype network (SPNet). The SPNet aggregates the lost information from global prototypes to create a supplemental prototype, which enhances the segmentation performance for the current class. In addition, we utilize mutual attention to enhance the similarity between the support and the query feature maps, allowing the model to better identify the target to be segmented. Finally, we introduce a Self-correcting auxiliary, which utilizes the data more effectively to improve segmentation accuracy. We conducted extensive experiments on PASCAL-5i and COCO-20i, which demonstrated the effectiveness of SPNet. And our method achieved state-of-the-art results in the 1-shot and 5-shot semantic segmentation settings.