Domain Adaptive Video Segmentation via Temporal Pseudo Supervision
نویسندگان
چکیده
AbstractVideo semantic segmentation has achieved great progress under the supervision of large amounts labelled training data. However, domain adaptive video segmentation, which can mitigate data labelling constraints by adapting from a source toward an unlabelled target domain, is largely neglected. We design temporal pseudo (TPS), simple and effective method that explores idea consistency for learning representations videos. Unlike traditional builds in spatial space, we explore spatiotemporal space enforcing model across augmented frames helps learn more diverse Specifically, cross-frame to provide previous while current frames. The encourages network produce high-certainty predictions, facilitates with augmentation effectively. Extensive experiments over multiple public datasets show TPS simpler implement, much stable train, achieves superior accuracy as compared state-of-the-art. Code available at https://github.com/xing0047/TPS.KeywordsVideo segmentationUnsupervised adaptationConsistency trainingPseudo labeling
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2022
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-20056-4_36