  FSS(Few-shot segmentation) aims to segment a target class using a small
number of labeled images(support set). To extract information relevant to the
target class, a dominant approach in best-performing FSS methods removes
background features using a support mask. We observe that this feature excision
through a limiting support mask introduces an information bottleneck in several
challenging FSS cases, e.g., for small targets and/or inaccurate target
boundaries. To this end, we present a novel method(MSI), which maximizes the
support-set information by exploiting two complementary sources of features to
generate super correlation maps. We validate the effectiveness of our approach
by instantiating it into three recent and strong FSS methods. Experimental
results on several publicly available FSS benchmarks show that our proposed
method consistently improves performance by visible margins and leads to faster
convergence. Our code and trained models are available at:
https://github.com/moonsh/MSI-Maximize-Support-Set-Information
