  The generalization capability of unsupervised domain adaptation can mitigate
the need for extensive pixel-level annotations to train semantic segmentation
networks by training models on synthetic data as a source with
computer-generated annotations. Entropy-based adversarial networks are proposed
to improve source domain prediction; however, they disregard significant
external information, such as edges, which have the potential to identify and
distinguish various objects within an image accurately. To address this issue,
we introduce a novel approach to domain adaptation, leveraging the synergy of
internal and external information within entropy-based adversarial networks. In
this approach, we enrich the discriminator network with edge-predicted
probability values within this innovative framework to enhance the clarity of
class boundaries. Furthermore, we devised a probability-sharing network that
integrates diverse information for more effective segmentation. Incorporating
object edges addresses a pivotal aspect of unsupervised domain adaptation that
has frequently been neglected in the past -- the precise delineation of object
boundaries. Conventional unsupervised domain adaptation methods usually center
around aligning feature distributions and may not explicitly model object
boundaries. Our approach effectively bridges this gap by offering clear
guidance on object boundaries, thereby elevating the quality of domain
adaptation. Our approach undergoes rigorous evaluation on the established
unsupervised domain adaptation benchmarks, specifically in adapting SYNTHIA
$\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Mapillary. Experimental
results show that the proposed model attains better performance than
state-of-the-art methods. The superior performance across different
unsupervised domain adaptation scenarios highlights the versatility and
robustness of the proposed method.
