Abstract
Deep learning models have achieved great success on various vision challenges, but a well-trained model would face drastic performance degradation when applied to unseen data. Since the model is sensitive to domain shift, unsupervised domain adaption attempts to reduce the domain gap and avoid costly annotation of unseen domains. This paper proposes a novel framework for cross-modality segmentation via similarity-based prototypes. In specific, we learn class-wise prototypes within an embedding space, then introduce a similarity constraint to make these prototypes representative for each semantic class while separable from different classes. Moreover, we use dictionaries to store prototypes extracted from different images, which prevents the class-missing problem and enables the contrastive learning of prototypes, and further improves performance. Extensive experiments show that our method achieves better results than other state-of-the-art methods.
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Ye, Z., Ju, C., Ma, C., Zhang, X. (2021). Unsupervised Domain Adaption via Similarity-Based Prototypes for Cross-Modality Segmentation. In: Albarqouni, S., et al. Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health. DART FAIR 2021 2021. Lecture Notes in Computer Science(), vol 12968. Springer, Cham. https://doi.org/10.1007/978-3-030-87722-4_13
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