  The purpose of segmentation refinement is to enhance the initial coarse masks
generated by segmentation algorithms. The refined masks are expected to capture
the details and contours of the target objects. Research on segmentation
refinement has developed as a response to the need for high-quality initial
masks. However, to our knowledge, no method has been developed that can
determine the success of segmentation refinement. Such a method could ensure
the reliability of segmentation in applications where the outcome of the
segmentation is important, and fosters innovation in image processing
technologies. To address this research gap, we propose JFS~(Judging From
Support-set), a method to identify the success of segmentation refinement
leveraging a few-shot segmentation (FSS) model. The traditional goal of the
problem in FSS is to find a target object in a query image utilizing target
information given by a support set. However, in our proposed method, we use the
FSS network in a novel way to assess the segmentation refinement. When there
are two masks, a coarse mask and a refined mask from segmentation refinement,
these two masks become support masks. The existing support mask works as a
ground truth mask to judge whether the quality of the refined segmentation is
more accurate than the coarse mask. We first obtained a coarse mask and refined
it using SEPL (SAM Enhanced Pseduo-Labels) to get the two masks. Then, these
become input to FSS model to judge whether the post-processing was successful.
JFS is evaluated on the best and worst cases from SEPL to validate its
effectiveness. The results showed that JFS can determine whether the SEPL is a
success or not.
