OurDB: Ouroboric Domain Bridging for Multi-Target Domain Adaptive
  Semantic Segmentation
Seungbeom Woo Geonwoo Baek Taehoon Kim Jaemin Na Joong-won Hwang Wonjun Hwang
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.