Look at the Neighbor: Distortion-aware Unsupervised Domain Adaptation
  for Panoramic Semantic Segmentation
Xu Zheng Tianbo Pan Yunhao Luo Lin Wang
Endeavors have been recently made to transfer knowledge from the labeled
pinhole image domain to the unlabeled panoramic image domain via Unsupervised
Domain Adaptation (UDA). The aim is to tackle the domain gaps caused by the
style disparities and distortion problem from the non-uniformly distributed
pixels of equirectangular projection (ERP). Previous works typically focus on
transferring knowledge based on geometric priors with specially designed
multi-branch network architectures. As a result, considerable computational
costs are induced, and meanwhile, their generalization abilities are profoundly
hindered by the variation of distortion among pixels. In this paper, we find
that the pixels' neighborhood regions of the ERP indeed introduce less
distortion. Intuitively, we propose a novel UDA framework that can effectively
address the distortion problems for panoramic semantic segmentation. In
comparison, our method is simpler, easier to implement, and more
computationally efficient. Specifically, we propose distortion-aware attention
(DA) capturing the neighboring pixel distribution without using any geometric
constraints. Moreover, we propose a class-wise feature aggregation (CFA) module
to iteratively update the feature representations with a memory bank. As such,
the feature similarity between two domains can be consistently optimized.
Extensive experiments show that our method achieves new state-of-the-art
performance while remarkably reducing 80% parameters.