  Existing few-shot segmentation (FSS) only considers learning support-query
correlation and segmenting unseen categories under the precise pixel masks.
However, the cost of a large number of pixel masks during training is
expensive. This paper considers a more challenging scenario, weakly-supervised
few-shot segmentation (WS-FSS), which only provides category ($i.e.$
image-level) labels. It requires the model to learn robust support-query
information when the generated mask is inaccurate. In this work, we design a
Correlation Enhancement Network (CORENet) with foundation model, which utilizes
multi-information guidance to learn robust correlation. Specifically,
correlation-guided transformer (CGT) utilizes self-supervised ViT tokens to
learn robust correlation from both local and global perspectives. From the
perspective of semantic categories, the class-guided module (CGM) guides the
model to locate valuable correlations through the pre-trained CLIP. Finally,
the embedding-guided module (EGM) implicitly guides the model to supplement the
inevitable information loss during the correlation learning by the original
appearance embedding and finally generates the query mask. Extensive
experiments on PASCAL-5$^i$ and COCO-20$^i$ have shown that CORENet exhibits
excellent performance compared to existing methods.
