  Despite great progress in supervised semantic segmentation,a large
performance drop is usually observed when deploying the model in the wild.
Domain adaptation methods tackle the issue by aligning the source domain and
the target domain. However, most existing methods attempt to perform the
alignment from a holistic view, ignoring the underlying class-level data
structure in the target domain. To fully exploit the supervision in the source
domain, we propose a fine-grained adversarial learning strategy for class-level
feature alignment while preserving the internal structure of semantics across
domains. We adopt a fine-grained domain discriminator that not only plays as a
domain distinguisher, but also differentiates domains at class level. The
traditional binary domain labels are also generalized to domain encodings as
the supervision signal to guide the fine-grained feature alignment. An analysis
with Class Center Distance (CCD) validates that our fine-grained adversarial
strategy achieves better class-level alignment compared to other
state-of-the-art methods. Our method is easy to implement and its effectiveness
is evaluated on three classical domain adaptation tasks, i.e., GTA5 to
Cityscapes, SYNTHIA to Cityscapes and Cityscapes to Cross-City. Large
performance gains show that our method outperforms other global feature
alignment based and class-wise alignment based counterparts. The code is
publicly available at https://github.com/JDAI-CV/FADA.
