Few-shot segmentation is a challenging task, requiring the extraction of a generalizable representation from only a few annotated samples, in order to segment novel query images. A common approach is to model each class with a single prototype. While conceptually simple, these methods suffer when the target appearance distribution is multi-modal or not linearly separable in feature space. To tackle this issue, we propose a few-shot learner formulation based on Gaussian process (GP) regression. Through the expressivity of the GP, our approach is capable of modeling complex appearance distributions in the deep feature space. The GP provides a principled way of capturing uncertainty, which serves as another powerful cue for the final segmentation, obtained by a CNN decoder. We further exploit the end-to-end learning capabilities of our approach to learn the output space of the GP learner, ensuring a richer encoding of the segmentation mask. We perform comprehensive experimental analysis of our few-shot learner formulation. Our approach sets a new state-of-the-art for 5-shot segmentation, with mIoU scores of 68.1 and 49.8 on PASCAL-5i and COCO-20i, respectively
翻译:微小截分是一个具有挑战性的任务,需要从仅有几个附加说明的样本中提取一般代表,以便分解新的查询图像。一个共同的方法是用一个原型来模拟每类的模型。在概念上简单,当目标外观分布在特性空间中是多式或非线性分离时,这些方法会受到影响。为了解决这一问题,我们建议根据高萨进程回归(GP)进行几发学习器配方。通过GP的表达性,我们的方法能够模拟深地特征空间的复杂外观分布。GP提供了一种有原则的捕捉不确定性的方法,这是由CNN调解码器获得的最后分割的又一个强有力的提示。我们进一步利用我们的方法的端到端学习能力,学习GP学习器的输出空间,确保分解面面面面罩的更紧密的编码。我们对我们微小的学习器配方进行全面的实验分析。我们的方法为5发相断面空间段设定了一个新的状态,MIU分号为68.1和49.8,分别是PASCAL-5和CO的MASAL-5和CO的分块。