论文标题
MSI：最大化支持集的信息以进行几次分割
MSI: Maximize Support-Set Information for Few-Shot Segmentation
论文作者
论文摘要
FSS（少量分割）旨在使用少量标记的图像（支持集）进行目标类细分。为了提取与目标类相关的信息，最佳表现FSS方法中的主要方法可以使用支持蒙版去除背景功能。我们观察到，通过限制支持掩码进行此功能切除，在几种具有挑战性的FSS案例中引入了信息瓶颈，例如针对小目标和/或目标界限。为此，我们提出了一种新颖的方法（MSI），该方法通过利用两个互补的特征来源来生成超相关图来最大程度地提高支持集信息。我们通过将方法实例化为最近和强大的FSS方法来验证我们的方法的有效性。几个公开可用的FSS基准测试的实验结果表明，我们提出的方法始终通过可见边缘提高性能，并导致更快的收敛性。我们的代码和训练有素的模型可在以下网址找到：https：//github.com/moonsh/msi-maximize-support-set-information
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
