Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation
Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation
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DOI：
10.1609/aaai.v33i01.33015581
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Yonghao Xu;Bo Du;Lefei Zhang;Qian Zhang;Guoli Wang;Liangpei Zhang
中科院分区：
文献类型：
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作者：
Yonghao Xu;Bo Du;Lefei Zhang;Qian Zhang;Guoli Wang;Liangpei Zhang
近年来，深度学习模型在语义分割方面取得了巨大成功。然而，由于域转移现象，这些模型可能无法很好地推广到看不见的图像域。由于像素级注释很难收集，因此开发能够将标记数据从源域适应到目标域的算法具有重要意义。为此，我们提出自集成注意力网络来减少不同数据集之间的领域差距。据我们所知，所提出的方法是首次尝试将自集成模型引入域适应以进行语义分割，这为如何学习域不变特征提供了不同的观点。此外，由于图像中的不同区域通常对应于不同级别的域间隙，因此我们将注意机制引入到所提出的框架中以生成注意感知特征，这些特征进一步用于指导目标域中一致性损失的计算。对两个基准数据集的实验表明，与最先进的方法相比，所提出的框架可以产生具有竞争力的性能。
Recent years have witnessed the great success of deep learning models in semantic segmentation. Nevertheless, these models may not generalize well to unseen image domains due to the phenomenon of domain shift. Since pixel-level annotations are laborious to collect, developing algorithms which can adapt labeled data from source domain to target domain is of great significance. To this end, we propose self-ensembling attention networks to reduce the domain gap between different datasets. To the best of our knowledge, the proposed method is the first attempt to introduce selfensembling model to domain adaptation for semantic segmentation, which provides a different view on how to learn domain-invariant features. Besides, since different regions in the image usually correspond to different levels of domain gap, we introduce the attention mechanism into the proposed framework to generate attention-aware features, which are further utilized to guide the calculation of consistency loss in the target domain. Experiments on two benchmark datasets demonstrate that the proposed framework can yield competitive performance compared with the state of the art methods.