CDAC: Cross-domain Attention Consistency in Transformer for Domain Adaptive Semantic Segmentation

Kaihong Wang, Donghyun Kim, Rogerio Feris, Margrit Betke; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 11519-11529

While transformers have greatly boosted performance in semantic segmentation, domain adaptive transformers are not yet well explored. We identify that the domain gap can cause discrepancies in self-attention. Due to this gap, the transformer attends to spurious regions or pixels, which deteriorates accuracy on the target domain. We propose Cross-Domain Attention Consistency (CDAC), to perform adaptation on attention maps using cross-domain attention layers that share features between source and target domains. Specifically, we impose consistency between predictions from cross-domain attention and self-attention modules to encourage similar distributions across domains in both the attention and output of the model, i.e., attention-level and output-level alignment. We also enforce consistency in attention maps between different augmented views to further strengthen the attention-based alignment. Combining these two components, CDAC mitigates the discrepancy in attention maps across domains and further boosts the performance of the transformer under unsupervised domain adaptation settings. Our method is evaluated on various widely used benchmarks and outperforms the state-of-the-art baselines, including GTAV-to-Cityscapes by 1.3 and 1.5 percent point (pp) and Synthia-to-Cityscapes by 0.6 pp and 2.9 pp when combining with two competitive Transformer-based backbones, respectively. Our code will be publicly available at https://github.com/wangkaihong/CDAC.