  Unsupervised Domain Adaptation (UDA) for semantic segmentation has been
favorably applied to real-world scenarios in which pixel-level labels are hard
to be obtained. In most of the existing UDA methods, all target data are
assumed to be introduced simultaneously. Yet, the data are usually presented
sequentially in the real world. Moreover, Continual UDA, which deals with more
practical scenarios with multiple target domains in the continual learning
setting, has not been actively explored. In this light, we propose Continual
UDA for semantic segmentation based on a newly designed Expanding
Target-specific Memory (ETM) framework. Our novel ETM framework contains
Target-specific Memory (TM) for each target domain to alleviate catastrophic
forgetting. Furthermore, a proposed Double Hinge Adversarial (DHA) loss leads
the network to produce better UDA performance overall. Our design of the TM and
training objectives let the semantic segmentation network adapt to the current
target domain while preserving the knowledge learned on previous target
domains. The model with the proposed framework outperforms other
state-of-the-art models in continual learning settings on standard benchmarks
such as GTA5, SYNTHIA, CityScapes, IDD, and Cross-City datasets. The source
code is available at https://github.com/joonh-kim/ETM.
