Hierarchical Instance Mixing across Domains in Aerial Segmentation
This work solves domain adaptation challenges for aerial segmentation, offering a novel approach but is incremental as it builds on existing mixing strategies.
The paper tackles unsupervised domain adaptation in aerial semantic segmentation by addressing domain imbalance and structural inconsistency, introducing Hierarchical Instance Mixing and a twin-head architecture, which outperforms state-of-the-art methods on the LoveDA benchmark.
We investigate the task of unsupervised domain adaptation in aerial semantic segmentation and discover that the current state-of-the-art algorithms designed for autonomous driving based on domain mixing do not translate well to the aerial setting. This is due to two factors: (i) a large disparity in the extension of the semantic categories, which causes a domain imbalance in the mixed image, and (ii) a weaker structural consistency in aerial scenes than in driving scenes since the same scene might be viewed from different perspectives and there is no well-defined and repeatable structure of the semantic elements in the images. Our solution to these problems is composed of: (i) a new mixing strategy for aerial segmentation across domains called Hierarchical Instance Mixing (HIMix), which extracts a set of connected components from each semantic mask and mixes them according to a semantic hierarchy and, (ii) a twin-head architecture in which two separate segmentation heads are fed with variations of the same images in a contrastive fashion to produce finer segmentation maps. We conduct extensive experiments on the LoveDA benchmark, where our solution outperforms the current state-of-the-art.