  Multi-target unsupervised domain adaptation (UDA) aims to learn a unified
model to address the domain shift between multiple target domains. Due to the
difficulty of obtaining annotations for dense predictions, it has recently been
introduced into cross-domain semantic segmentation. However, most existing
solutions require labeled data from the source domain and unlabeled data from
multiple target domains concurrently during training. Collectively, we refer to
this data as "external". When faced with new unlabeled data from an unseen
target domain, these solutions either do not generalize well or require
retraining from scratch on all data. To address these challenges, we introduce
a new strategy called "multi-target UDA without external data" for semantic
segmentation. Specifically, the segmentation model is initially trained on the
external data. Then, it is adapted to a new unseen target domain without
accessing any external data. This approach is thus more scalable than existing
solutions and remains applicable when external data is inaccessible. We
demonstrate this strategy using a simple method that incorporates
self-distillation and adversarial learning, where knowledge acquired from the
external data is preserved during adaptation through "one-way" adversarial
learning. Extensive experiments in several synthetic-to-real and real-to-real
adaptation settings on four benchmark urban driving datasets show that our
method significantly outperforms current state-of-the-art solutions, even in
the absence of external data. Our source code is available online
(https://github.com/YonghaoXu/UT-KD).
