Semantic segmentation plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. Yet, the state-of-the-art models rely on large amount of annotated samples, which are more expensive to obtain than in tasks such as image classification. Since unlabelled data is instead significantly cheaper to obtain, it is not surprising that Unsupervised Domain Adaptation reached a broad success within the semantic segmentation community. This survey is an effort to summarize five years of this incredibly rapidly growing field, which embraces the importance of semantic segmentation itself and a critical need of adapting segmentation models to new environments. We present the most important semantic segmentation methods; we provide a comprehensive survey on domain adaptation techniques for semantic segmentation; we unveil newer trends such as multi-domain learning, domain generalization, test-time adaptation or source-free domain adaptation; we conclude this survey by describing datasets and benchmarks most widely used in semantic segmentation research. We hope that this survey will provide researchers across academia and industry with a comprehensive reference guide and will help them in fostering new research directions in the field.
翻译:语义分解在广泛的计算机视觉应用中发挥着根本作用,为全球了解图像提供了关键信息。然而,最先进的模型依赖于大量附加说明的样本,这些样本比图像分类等任务更昂贵。由于未贴标签的数据比图像分类要便宜得多,因此获得无标签数据的成本要低得多,不足为奇的是,未受监管的域域适应在语义分解界界中取得了广泛成功。这项调查旨在总结这一惊人快速增长的域的五年情况,其中包括语义分解本身的重要性,以及使分解模型适应新环境的迫切需要。我们提出了最重要的语义分解方法;我们提供了关于用于语义分解的域适应技术的全面调查;我们公布了多域学习、域化、测试时间适应或无源域适应等新趋势;我们通过描述在语义分解分解研究中最广泛使用的数据集和基准来结束这项调查。我们希望这项调查将为学术界和工业界的研究人员提供全面的参考指南,并将帮助他们促进新的研究方向。