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- W4313019879 abstract "In computer vision, data-driven convolutional neural networks could learn increasingly rich semantic features of images. However, manual annotation of images is an expensive and time-consuming task that hinders development. As a branch of unsupervised learning, self-supervised learning does not rely on labels, avoiding the work of labeling the data. This paper provides a comprehensive discussion of the development of self-supervised learning in computer vision. First, we briefly describes the motivation for proposing self-supervised learning and related concepts, introduces the self-supervised learning paradigm from three aspects, describes the applications of self-supervised learning in computer vision, and finally provides a summary and an outlook on its future development." @default.
- W4313019879 created "2023-01-05" @default.
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- W4313019879 date "2022-01-01" @default.
- W4313019879 modified "2023-09-29" @default.
- W4313019879 title "Self-supervised Learning in Computer Vision: A Review" @default.
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- W4313019879 doi "https://doi.org/10.1007/978-981-19-6901-0_116" @default.
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