POEM: A prototype cross and emphasis network for few-shot semantic segmentation

Abstract

To tackle the demand for large labeled datasets and poor unseen class generalization for segmentation tasks, few-shot segmentation (FSS) has received widespread attention, which utilizes prototypical learning to achieve significant progress. However, the prototypes generated by averaging the global object information still suffer from the appearance diversity within the target class. Motivated by the fact that humans only need part attributes rather than all attributions to recognize objects from a particular class, we propose an effective prototype cross and emphasis segmentation network (POEM) to provide more precise guidance in the feature matching process. Firstly, we present a regional crossed referencing module to improve the prototypical learning strategy, which mines the relevant attributes of the target objects in both support and query images and customizes the prototypes for the query images. Secondly, we develop a foreground-emphasized reasoning module to reduce the negative influence caused by complex background clutter, in which the foreground and background attributions are regarded as fundamental information and auxiliary information, respectively. Extensive experiments on two standard datasets have demonstrated the superiority of POEM over the state-of-the-art methods.