SELF-COMPENSATING LEARNING FOR FEW-SHOT SEGMENTATION

Few-shot segmentation (FSS) has witnessed rapid development. Most existing approaches extract prototypes from support images to segment query images. However, the integrity and validity of these support prototypes cannot be guaranteed. To solve the above drawbacks, we propose a self-compensating strategy, aiming to provide query-aware support information, to build more effective matching between support information and query images. Specifically, we design a prototype compensating module to mine useful information from the query prediction, to update original support prototypes as new query-aware support prototypes. Then the updated prototypes are utilized to perform the second matching with query features. In addition, we also compensate the information of original prior masks on the second matching phase, to improve the quality of prior masks. With improved prototype representations and prior knowledge, our approach can directly improve the performance of different approaches with new state-of-the-art performances.