Abstract
Weakly supervised semantic segmentation (WSSS) with image-level labels intends to achieve dense tasks without laborious annotations. However, due to the ambiguous contexts and fuzzy regions, the performance of WSSS, especially the stages of generating Class Activation Maps (CAMs) and refining pseudo masks, widely suffers from ambiguity while being barely noticed by previous literature. In this work, we propose UniA, a unified single-staged WSSS framework, to efficiently tackle this issue from the perspective of uncertainty inference and affinity diversification, respectively. When activating class objects, we argue that the false activation stems from the bias to the ambiguous regions during the feature extraction. Therefore, we design a more robust feature representation with a probabilistic Gaussian distribution and introduce the uncertainty estimation to avoid the bias. A distribution loss is particularly proposed to supervise the process, which effectively captures the ambiguity and models the complex dependencies among features. When refining pseudo labels, we observe that the affinity from the prevailing refinement methods intends to be similar among ambiguities. To this end, an affinity diversification module is proposed to promote diversity among semantics. A mutual complementing refinement is proposed to initially rectify the ambiguous affinity with multiple inferred pseudo labels. More importantly, a contrastive affinity loss is further designed to diversify the relations among unrelated semantics, which reliably propagates the diversity into the whole feature representations and helps generate better pseudo masks. Extensive experiments are conducted on PASCAL VOC, MS COCO, and medical ACDC datasets, which validate the efficiency of UniA tackling ambiguity and the superiority over recent single-staged or even most multi-staged competitors.
Abstract (translated)
弱监督语义分割（WSSS）采用图像级标签的目的是实现密集任务，而不需要费力地标注。然而，由于模糊的上下文和模糊区域，WSSS的性能，尤其是生成类激活图（CAM）和改进伪掩码的阶段，在很大程度上受到模糊性的影响，尽管在之前文献中 barely 被注意到。在本文中，我们提出UniA，一种统一的一阶段WSSS框架，从不确定性推理和异质扩展的角度来解决这个问题。当激活类别物体时，我们认为是由于在特征提取过程中对模糊区域的偏见导致的假激活。因此，我们设计了一个具有概率高斯分布的更稳健的特征表示，并引入不确定性估计来避免偏见。特别地，提出了分布损失来指导过程，有效地捕捉了不确定性和建模了特征之间的复杂关系。当优化伪标签时，我们观察到当前优化方法之间的异质性意图相似。为此，我们提出了一个异质化模块来促进语义之间的多样性。互为补充的优化被提出作为首先通过多个推断伪标签初始化模糊异质性的纠正。此外，还进一步设计了一个对比性异质性损失，以进一步分散无关语义之间的关系，可靠地将多样性传播到整个特征表示中，并帮助生成更优秀的伪掩码。在PASCAL VOC、MS COCO和医疗ACDC数据集上进行了广泛的实验，验证了UniA解决不确定性和优越性以及与最近单阶段或甚至是多阶段竞争者相比的效率。
URL
https://arxiv.org/abs/2404.08195
