Multi-target domain adaptation (MTDA) for semantic segmentation poses a significant challenge, as it involves multiple target domains with varying distributions. The goal of MTDA is to minimize the domain discrepancies among a single source and multi-target domains, aiming to train a single model that excels across all target domains. Previous MTDA approaches typically employ multiple teacher architectures, where each teacher specializes in one target domain to simplify the task. However, these architectures hinder the student model from fully assimilating comprehensive knowledge from all target-specific teachers and escalate training costs with increasing target domains. In this paper, we propose an ouroboric domain bridging (OurDB) framework, offering an efficient solution to the MTDA problem using a single teacher architecture. This framework dynamically cycles through multiple target domains, aligning each domain individually to restrain the biased alignment problem, and utilizes Fisher information to minimize the forgetting of knowledge from previous target domains. We also propose a context-guided class-wise mixup (CGMix) that leverages contextual information tailored to diverse target contexts in MTDA. Experimental evaluations conducted on four urban driving datasets (i.e., GTA5, Cityscapes, IDD, and Mapillary) demonstrate the superiority of our method over existing state-of-the-art approaches.

多目标域自适应（MTDA）的研究挑战在于多个具有不同分布的目标域。本文提出了一种以单个教师架构为基础的MTDA框架，通过动态循环处理多个目标域，并利用Fisher信息来最小化对前面目标域知识的遗忘。同时，我们还提出了一种针对多目标域的上下文引导的类别混合技术（CGMix）。在四个城市驾驶数据集（GTA5、Cityscapes、IDD和Mapillary）上的实证评估表明，我们的方法优于现有的最先进方法。

OurDB: 多目标领域自适应语义分割的Ouroboric域桥接