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：弱监督下的标签高效稀疏标注分割方法因能降低繁琐的像素级标注成本而受到越来越多的研究关注，其中成对亲和建模技术在该任务中扮演着关键角色。现有方法大多采用局部外观核来建模邻域成对势能。然而，这种局部操作无法捕获长程依赖关系，且忽略了目标对象的拓扑结构。本研究将亲和建模形式化为一个亲和传播过程，并提出局部与全局两种成对亲和项以生成准确的软伪标签。同时开发了一种高效算法来显著降低计算成本。所提方法可便捷地嵌入现有分割网络。在三种典型的标签高效分割任务（即框监督实例分割、点/涂鸦监督语义分割及CLIP引导语义分割）上的实验表明，该方法具有优越的性能。