  The key to the success of few-shot segmentation (FSS) lies in how to
effectively utilize support samples. Most solutions compress support foreground
(FG) features into prototypes, but lose some spatial details. Instead, others
use cross attention to fuse query features with uncompressed support FG. Query
FG could be fused with support FG, however, query background (BG) cannot find
matched BG features in support FG, yet inevitably integrates dissimilar
features. Besides, as both query FG and BG are combined with support FG, they
get entangled, thereby leading to ineffective segmentation. To cope with these
issues, we design a self-calibrated cross attention (SCCA) block. For efficient
patch-based attention, query and support features are firstly split into
patches. Then, we design a patch alignment module to align each query patch
with its most similar support patch for better cross attention. Specifically,
SCCA takes a query patch as Q, and groups the patches from the same query image
and the aligned patches from the support image as K&V. In this way, the query
BG features are fused with matched BG features (from query patches), and thus
the aforementioned issues will be mitigated. Moreover, when calculating SCCA,
we design a scaled-cosine mechanism to better utilize the support features for
similarity calculation. Extensive experiments conducted on PASCAL-5^i and
COCO-20^i demonstrate the superiority of our model, e.g., the mIoU score under
5-shot setting on COCO-20^i is 5.6%+ better than previous state-of-the-arts.
The code is available at https://github.com/Sam1224/SCCAN.
