Annotation-scarce semantic segmentation aims to obtain meaningful pixel-level discrimination with scarce or even no manual annotations, of which the crux is how to utilize unlabeled data by pseudo-label learning. Typical works focus on ameliorating the error-prone pseudo-labeling, e.g., only utilizing high-confidence pseudo labels and filtering low-confidence ones out. But we think differently and resort to exhausting informative semantics from multiple probably correct candidate labels. This brings our method the ability to learn more accurately even though pseudo labels are unreliable. In this paper, we propose Adaptive Fuzzy Positive Learning (A-FPL) for correctly learning unlabeled data in a plug-and-play fashion, targeting adaptively encouraging fuzzy positive predictions and suppressing highly probable negatives. Specifically, A-FPL comprises two main components: (1) Fuzzy positive assignment (FPA) that adaptively assigns fuzzy positive labels to each pixel, while ensuring their quality through a T-value adaption algorithm (2) Fuzzy positive regularization (FPR) that restricts the predictions of fuzzy positive categories to be larger than those of negative categories. Being conceptually simple yet practically effective, A-FPL remarkably alleviates interference from wrong pseudo labels, progressively refining semantic discrimination. Theoretical analysis and extensive experiments on various training settings with consistent performance gain justify the superiority of our approach. Codes are at A-FPL.

注释稀缺语义分割旨在在很少甚至没有手动注释的情况下获得有意义的像素级区分，其关键在于如何通过伪标签学习来利用未标记的数据。典型的工作集中在改善容易出错的伪标签，例如，仅利用高置信度的伪标签并过滤掉低置信度的伪标签。但我们的想法不同，并诉诸于从多个可能正确的候选标签中穷尽信息语义。即使伪标签不可靠，这也使我们的方法能够更准确地学习。在本文中，我们提出了自适应模糊正学习（A-FPL），用于以即插即用的方式正确学习未标记的数据，目标是自适应地鼓励模糊正预测并抑制高概率的负预测。具体来说，A-FPL 包括两个主要组成部分：（1）模糊正分配（FPA），自适应地为每个像素分配模糊正标签，同时通过 T 值自适应算法确保其质量（2）模糊正正则化（FPR），限制模糊正类别的预测大于负类别的预测。 A-FPL 概念简单但实际上有效，显着减轻了错误伪标签的干扰，逐步完善了语义区分。对各种训练环境进行的理论分析和广泛的实验以及一致的性能增益证明了我们方法的优越性。代码位于 A-FPL。