Self-regularized prototypical network for few-shot semantic segmentation

•We propose a direct yet effective self-regularization module. Prototypes are generated, evaluated, and regularized under the supervision of support masks, which differs from existing works.•We adopt fidelity as the distance metric in prototype generation for the first time, which provides a more evident distinction for vectors.•We adopt an iterative query inference module, which uses a collection of prototypes for segmentation and improves the generalization ability for query inference.•We achieve new state-of-the-art performance on two few-shot segmentation benchmarks.