In this work, we introduce a new concept, named source-free open compound domain adaptation (SF-OCDA), and study it in semantic segmentation. SF-OCDA is more challenging than the traditional domain adaptation but it is more practical. It jointly considers (1) the issues of data privacy and data storage and (2) the scenario of multiple target domains and unseen open domains. In SF-OCDA, only the source pre-trained model and the target data are available to learn the target model. The model is evaluated on the samples from the target and unseen open domains. To solve this problem, we present an effective framework by separating the training process into two stages: (1) pre-training a generalized source model and (2) adapting a target model with self-supervised learning. In our framework, we propose the Cross-Patch Style Swap (CPSS) to diversify samples with various patch styles in the feature-level, which can benefit the training of both stages. First, CPSS can significantly improve the generalization ability of the source model, providing more accurate pseudo-labels for the latter stage. Second, CPSS can reduce the influence of noisy pseudo-labels and also avoid the model overfitting to the target domain during self-supervised learning, consistently boosting the performance on the target and open domains. Experiments demonstrate that our method produces state-of-the-art results on the C-Driving dataset. Furthermore, our model also achieves the leading performance on CityScapes for domain generalization.
翻译:在这项工作中,我们引入了一个新的概念,名为无源开放化合物域适应(SF-OCDA),并在语义分割中研究。SF-OCDA比传统的域适应更具挑战性,但更实际。它共同审议了:(1)数据隐私和数据储存问题,(2)多目标域和无形开放域的设想。在SF-OCDA中,只有源预培训模型和目标数据可用于学习目标模型。该模型在目标域和无形开放域的样本上进行评估。为了解决这一问题,我们提出了一个有效的框架,将培训进程分为两个阶段:(1) 预先培训一个通用源模型,(2) 以自我监督的学习方式调整一个目标模型。在我们的框架内,我们建议跨批标准样式交换(CPSS)使样本多样化,具有不同功能级的补差风格,这有利于对两个阶段的培训。首先,CPSS模型可以显著提高源模型的普及能力,为后一个阶段提供更准确的伪标签。第二,CPSSD可以减少对主域源域源域模式的影响,在持续推进目标域的自我升级,同时演示C-升级模型。