Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers

With the increasing availability of depth sensors, multimodal frameworks that combine color information with depth data are attracting increasing interest. In the challenging task of semantic segmentation, depth maps allow to distinguish between similarly colored objects at different depths and provide useful geometric cues. On the other side, ground truth data for semantic segmentation is burdensome to be provided and thus domain adaptation is another significant research area. Specifically, we address the challenging source-free domain adaptation setting where the adaptation is performed without reusing source data. We propose MISFIT: MultImodal Source-Free Information fusion Transformer, a depth-aware framework which injects depth information into a segmentation module based on vision transformers at multiple stages, namely at the input, feature and output levels. Color and depth style transfer helps early-stage domain alignment while re-wiring self-attention between modalities creates mixed features allowing the extraction of better semantic content. Furthermore, a depth-based entropy minimization strategy is also proposed to adaptively weight regions at different distances. Our framework, which is also the first approach using vision transformers for source-free semantic segmentation, shows noticeable performance improvements with respect to standard strategies.