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.

翻译：摘要：多目标域自适应语义分割因涉及分布各异的多个目标域而极具挑战性，其目标在于最小化单一源域与多目标域之间的域差异，从而训练出能在所有目标域中表现优异的单模型。现有方法通常采用多教师架构，通过为每个目标域配备专属教师以简化任务，但这种架构会阻碍学生模型充分吸收所有目标域教师的综合知识，且随目标域数量增加导致训练成本攀升。本文提出循环域桥接框架（OurDB），以单教师架构高效解决多目标域自适应问题。该框架通过动态循环遍历多个目标域，对每个域进行独立对齐以抑制偏置对齐问题，并利用Fisher信息最小化对先前目标域知识的遗忘。我们进一步提出上下文引导的类级混合增强（CGMix），该技术可针对多目标域自适应中多样的目标上下文进行定制化利用。在四个城市驾驶数据集（GTA5、Cityscapes、IDD及Mapillary）上的实验表明，本方法显著优于现有最先进技术。