Abstract: Few-shot semantic segmentation aims to perform pixel-level classification tasks under conditions of limited annotations. To further enhance the generalization ability of few-shot semantic segmentation based on prototype networks for unseen classes, we proposed a method based on self-correlation reinforcement and prototype supervision, addressing the issues of appearance discrepancies between support samples and query images and poor prototype quality. Firstly, we designed a self-correlation reinforcement module that leveraged the self-correlation of pixels within the query image to transfer initial auxiliary priors to the query data, generating high-level class prototypes that provided reinforced prior information with high discriminative power. Secondly, we introduced multiple progressive supervision losses, using the prototype's ability to recover the support mask as a supervision indicator for prototype quality. This allowed for self-regularized updates of the prototypes and self-matching updates of the auxiliary priors, effectively enhancing the prototype's ability to generalize from the support information and encouraging the auxiliary priors to retain more query-relevant details. The proposed method was validated on the few-shot benchmark dataset PASCAL-5i. Experimental results show that, under the 1-shot setting, the method achieves an mIoU(mean intersection over union) of 64.4% and an FB-IoU(foreground-background intersection over union) of 73.5%, demonstrating its effectiveness and advanced nature.