Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation

By Yonghao Xu, Bo Du, Lefei Zhang, Qian Zhang, Guoli Wang, Liangpei Zhang. 2019

75 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Incremental Object Detection via Meta-Learning (2020, neutral): “This has found success in semi-supervised learning [50], [51], [52] and domain adaptation [53], [54], [55], [56] settings.”
Semi-supervised Relation Extraction via Incremental Meta Self-Training (2020, methodology): “One major category is the self-ensembling method (French et al., 2017; Xu et al., 2019), which is based on the assumption that pseudo label distributions should remain unchanged even if the model parameters and instances have small perturbations.”
Self-Ensembling With GAN-Based Data Augmentation for Domain Adaptation in Semantic Segmentation (2019, methodology): “[48] utilize the self-ensembling attention network to extract attentionaware features for domain adaptation.”