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.

翻译：分割细化的目的是改进由分割算法生成的初始粗糙掩码。细化后的掩码应能捕捉目标对象的细节与轮廓。分割细化研究的发展源于对高质量初始掩码的需求。然而，据我们所知，目前尚未开发出能够判定分割细化是否成功的方法。这种方法可确保在分割结果至关重要的应用场景中分割的可靠性，并推动图像处理技术的创新。为填补这一研究空白，我们提出JFS（基于支持集判定）方法，该方法利用少样本分割模型来识别分割细化的成功与否。传统少样本分割问题的目标是通过利用支持集提供的目标信息，在查询图像中定位目标对象。但在我们提出的方法中，我们创新性地使用FSS网络来评估分割细化。当存在两个掩码（即分割细化产生的粗糙掩码与细化掩码）时，这两个掩码将作为支持掩码。现有支持掩码作为真实掩码，用于判断细化分割的质量是否比粗糙掩码更精确。我们首先获取粗糙掩码，并使用SEPL（SAM增强伪标签）进行细化得到两个掩码，随后将其输入FSS模型以判定后处理是否成功。JFS在SEPL的最佳与最差案例上进行评估以验证其有效性。结果表明，JFS能够准确判定SEPL处理的成败。