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arXiv · 2210.06216

Hierarchical Instance Mixing across Domains in Aerial Segmentation

Edoardo Arnaudo·Antonio Tavera·Fabrizio Dominici·Carlo Masone·Barbara Caputo
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Abstract

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.

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Edoardo Arnaudo, Antonio Tavera, Fabrizio Dominici, Carlo Masone, Barbara Caputo. 2022-10-12. Hierarchical Instance Mixing across Domains in Aerial Segmentation. https://arxiv.org/abs/2210.06216

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