Weakly-supervised segmentation with label-efficient sparse annotations has attracted increasing research attention to reduce the cost of laborious pixel-wise labeling process, while the pairwise affinity modeling techniques play an essential role in this task. Most of the existing approaches focus on using the local appearance kernel to model the neighboring pairwise potentials. However, such a local operation fails to capture the long-range dependencies and ignores the topology of objects. In this work, we formulate the affinity modeling as an affinity propagation process, and propose a local and a global pairwise affinity terms to generate accurate soft pseudo labels. An efficient algorithm is also developed to reduce significantly the computational cost. The proposed approach can be conveniently plugged into existing segmentation networks. Experiments on three typical label-efficient segmentation tasks, i.e. box-supervised instance segmentation, point/scribble-supervised semantic segmentation and CLIP-guided semantic segmentation, demonstrate the superior performance of the proposed approach.

翻译：弱监督分割结合标签高效稀疏标注的方法，因能降低繁琐的逐像素标注成本而受到越来越多研究关注，其中成对亲和力建模技术在此任务中发挥关键作用。现有方法大多利用局部外观核来建模相邻成对势能。然而，这种局部操作无法捕获长距离依赖关系，且忽略了目标拓扑结构。本文中，我们将亲和力建模表述为一个亲和力传播过程，并提出了局部和全局两种成对亲和力项以生成准确的软伪标签。同时，我们开发了一种高效算法以显著降低计算成本。所提出的方法可便捷地嵌入现有分割网络中。在三种典型的高效标签分割任务（即基于框监督的实例分割、基于点/涂鸦监督的语义分割以及CLIP引导的语义分割）上的实验表明，该方法具有优越的性能。