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Few-shot semantic segmentation aims at recognizing the object regions ofunseen categories with only a few annotated examples as supervision. The key tofew-shot segmentation is to establish a robust semantic relationship betweenthe support and query images and to prevent overfitting. In this paper, wepropose an effective Multi-similarity Hyperrelation Network (MSHNet) to tacklethe few-shot semantic segmentation problem. In MSHNet, we propose a newGenerative Prototype Similarity (GPS), which together with cosine similaritycan establish a strong semantic relation between the support and query images.The locally generated prototype similarity based on global feature is logicallycomplementary to the global cosine similarity based on local feature, and therelationship between the query image and the supported image can be expressedmore comprehensively by using the two similarities simultaneously. In addition,we propose a Symmetric Merging Block (SMB) in MSHNet to efficiently mergemulti-layer, multi-shot and multi-similarity hyperrelational features. MSHNetis built on the basis of similarity rather than specific category features,which can achieve more general unity and effectively reduce overfitting. On twobenchmark semantic segmentation datasets Pascal-5i and COCO-20i, MSHNetachieves new state-of-the-art performances on 1-shot and 5-shot semanticsegmentation tasks.

Multi-similarity based Hyperrelation Network for few-shot segmentation