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.

无监督领域适应的研究中，我们提出了一种新的方法，通过在熵对抗网络中丰富判别器网络的边缘预测概率值，结合内部和外部信息以提高对象边界的清晰度，进而改进了领域适应质量。实验证明，该方法在多种无监督领域适应场景中具有出色的性能。