Abstract
Unlike fully supervised semantic segmentation, weakly supervised semantic segmentation (WSSS) relies on weaker forms of supervision to perform dense prediction tasks. Among the various types of weak supervision, WSSS with image level annotations is considered both the most challenging and the most practical, attracting significant research attention. Therefore, in this review, we focus on WSSS with image level annotations. Additionally, this review concentrates on mainstream research directions, deliberately omitting less influential branches. Given the rapid development of new methods and the limitations of existing surveys in capturing recent trends, there is a pressing need for an updated and comprehensive review. Our goal is to fill this gap by synthesizing the latest advancements and state-of-the-art techniques in WSSS with image level labels. Basically, we provide a comprehensive review of recent advancements in WSSS with image level labels, categorizing existing methods based on the types and levels of additional supervision involved. We also examine the challenges of applying advanced methods to domain specific datasets in WSSS,a topic that remains underexplored. Finally, we discuss the current challenges, evaluate the limitations of existing approaches, and outline several promising directions for future research. This review is intended for researchers who are already familiar with the fundamental concepts of WSSS and are seeking to deepen their understanding of current advances and methodological innovations.
Abstract (translated)
与完全监督的语义分割相比，弱监督语义分割（WSSS）依赖于较弱形式的监督来执行密集预测任务。在各种类型的弱监督中，带有图像级别注释的WSSS被认为是最具挑战性和最实用的，因此吸引了大量研究关注。因此，在这篇综述中，我们将重点放在带有图像级别注释的WSSS上。此外，该综述专注于主流的研究方向，并有意省略了影响力较小的方向。鉴于新方法迅速发展以及现有调查在捕捉最新趋势方面的局限性，迫切需要更新和全面的回顾。我们的目标是通过综合最新的进展和最先进的技术来填补这一空白，特别是在带有图像级别标签的WSSS领域。 基本上，我们提供了最近在带有图像级别标签的WSSS领域的综合回顾，并根据额外监督的类型和程度对现有方法进行了分类。此外，我们将探讨将高级方法应用于特定领域数据集时所面临的挑战，在该领域中此话题尚未得到充分研究。最后，我们将讨论当前的挑战、评估现有方法的局限性，并概述未来有前景的研究方向。 这篇综述旨在为已经熟悉WSSS基本概念并希望深入了解最新进展和方法创新的研究人员提供帮助。
URL
https://arxiv.org/abs/2507.21587
