  Recent endeavors have been made to leverage self-supervised depth estimation
as guidance in unsupervised domain adaptation (UDA) for semantic segmentation.
Prior arts, however, overlook the discrepancy between semantic and depth
features, as well as the reliability of feature fusion, thus leading to
suboptimal segmentation performance. To address this issue, we propose a novel
UDA framework called SMART (croSs doMain semAntic segmentation based on eneRgy
esTimation) that utilizes Energy-Based Models (EBMs) to obtain task-adaptive
features and achieve reliable feature fusion for semantic segmentation with
self-supervised depth estimates. Our framework incorporates two novel
components: energy-based feature fusion (EB2F) and energy-based reliable fusion
Assessment (RFA) modules. The EB2F module produces task-adaptive semantic and
depth features by explicitly measuring and reducing their discrepancy using
Hopfield energy for better feature fusion. The RFA module evaluates the
reliability of the feature fusion using an energy score to improve the
effectiveness of depth guidance. Extensive experiments on two datasets
demonstrate that our method achieves significant performance gains over prior
works, validating the effectiveness of our energy-based learning approach.
