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Segment scene in new domain with semi-supervised learning by intra-domain target information with Fu

Segment scene in new domain with semi-supervised learning by intra-domain target information with Fu


Semi-supervised Domain Adaptation with Inter and Intra-domain Mixing for Semantic Segmentation

arXiv paper abstract https://arxiv.org/abs/2308.15855



Despite recent advances in semantic segmentation, an inevitable challenge is the performance degradation caused by the domain shift in real application.


... semi-supervised domain adaptation (SSDA) has been proposed ... focus on leveraging the unlabeled target data and source data.


... highlight the significance of exploiting the intra-domain information between the limited labeled target data and unlabeled target data, as it greatly benefits domain adaptation.


Instead of solely using the scarce labeled data for supervision, ... propose a novel SSDA framework that incorporates both inter-domain mixing and intra-domain mixing, where inter-domain mixing mitigates the source-target domain gap and intra-domain mixing enriches the available target domain information.


By simultaneously learning from inter-domain mixing and intra-domain mixing, the network can capture more domain-invariant features and promote its performance on the target domain.


... demonstrate the effectiveness of ... method, surpassing previous methods by a large margin.



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