For the few-shot segmentation (FSS) task, existing methods
attempt to capture the diversity of new classes by fully uti-
lizing the limited support images, such as cross-attention and
prototype matching. However, they often overlook the fact
that there is variability in different regions of the same ob-
ject, and intra-image similarity is higher than inter-image sim-
ilarity.To address these limitations, a Self-Guided Trans-
former (SGT) is proposed by leveraging intra-image similar-
ity to improve intra-object inconsistencies in this paper. The
proposed SGT can selectively guide segmentation, emphasiz-
ing the regions that are easily distinguishable while adapting
to the challenges caused by less discriminative regions within
objects. Through a refined feature interaction scheme and the
novel SGT module, our method can achieve state-of-the-art
performance on various FSS datasets, demonstrating signifi-
cant advances in few-shot semantic segmentation. The code
is publicly available at https://github.com/HuHaigen/SGT.