Hierarchical Instance Mixing across Domains in Aerial Segmentation
- URL: http://arxiv.org/abs/2210.06216v1
- Date: Wed, 12 Oct 2022 14:02:20 GMT
- Title: Hierarchical Instance Mixing across Domains in Aerial Segmentation
- Authors: Edoardo Arnaudo, Antonio Tavera, Fabrizio Dominici, Carlo Masone,
Barbara Caputo
- Abstract summary: We develop a new strategy for aerial segmentation across domains called Hierarchical Instance Mixing (HIMix)
We conduct extensive experiments on the LoveDA benchmark, where our solution outperforms the current state-of-the-art.
- Score: 14.738954189759156
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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