This paper aims to address few-shot segmentation. While existing prototype-based methods have achieved considerable success, they suffer from uncertainty and ambiguity caused by limited labeled examples. In this work, we propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot segmentation. We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution. The probabilistic modeling of the prototype enhances the model's generalization ability by handling the inherent uncertainty caused by limited data and intra-class variations of objects. To further enhance the model, we introduce a local latent variable to represent the attention map of each query image, which enables the model to attend to foreground objects while suppressing the background. The optimization of the proposed model is formulated as a variational Bayesian inference problem, which is established by amortized inference networks. We conduct extensive experiments on four benchmarks, where our proposal obtains at least competitive and often better performance than state-of-the-art prototype-based methods. We also provide comprehensive analyses and ablation studies to gain insight into the effectiveness of our method for few-shot segmentation.

翻译：本文旨在解决少样本分割问题。尽管现有基于原型的方法已取得显著成功，但它们受限于标注样本不足所导致的模糊性和不确定性。本研究提出关注型原型推断（API），一种用于少样本分割的概率潜变量框架。我们定义全局潜变量以表征每个对象类别的原型，并将其建模为概率分布。这种原型概率建模通过处理有限数据及对象类内变异带来的固有不确定性，增强了模型的泛化能力。为进一步优化模型，我们引入局部潜变量以表征每张查询图像的注意力图，使模型能够关注前景对象同时抑制背景。所提模型的优化被表述为变分贝叶斯推断问题，并通过摊销推断网络实现。我们在四个基准数据集上开展广泛实验，结果表明我们的方法至少与最先进的基于原型的方法性能相当，通常更优。我们还提供全面的分析与消融实验，以深入理解本方法在少样本分割中的有效性。