  In this paper, we present the submission to the 5th Annual Smoky Mountains
Computational Sciences Data Challenge, Challenge 3. This is the solution for
semantic segmentation problem in both real-world and synthetic images from a
vehicle s forward-facing camera. We concentrate in building a robust model
which performs well across various domains of different outdoor situations such
as sunny, snowy, rainy, etc. In particular, our method is developed with two
main directions: model development and domain adaptation. In model development,
we use the High Resolution Network (HRNet) as the baseline. Then, this baseline
s result is processed by two coarse-to-fine models: Object-Contextual
Representations (OCR) and Hierarchical Multi-scale Attention (HMA) to get the
better robust feature. For domain adaption, we implement the Domain-Based Batch
Normalization (DNB) to reduce the distribution shift from diverse domains. Our
proposed method yield 81.259 mean intersection-over-union (mIoU) in validation
set. This paper studies the effectiveness of employing real-world and synthetic
data to handle the domain adaptation in semantic segmentation problem.
