Abstract
Image semantic segmentation is one of the most important tasks in the field of computer vision, and it has made great progress in many applications. Many fully supervised deep learning models are designed to implement complex semantic segmentation tasks and the experimental results are remarkable. However, the acquisition of pixel-level labels in fully supervised learning is time consuming and laborious, semi-supervised and weakly supervised learning is gradually replacing fully supervised learning, thus achieving good results at a lower cost. Based on the commonly used models such as convolutional neural networks, fully convolutional networks, generative adversarial networks, this paper focuses on the core methods and reviews the semi- and weakly supervised semantic segmentation models in recent years. In the following chapters, existing evaluations and data sets are summarized in details and the experimental results are analyzed according to the data set. The last part of the paper is an objective summary. In addition, it points out the possible direction of research and inspiring suggestions for future work.
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Funding was provided by State’s Key Project of Research and Development Plan of China (Grant No. 2016YFC0600900), National Natural Science Foundation of China (Grant Nod. 61572505, 61772530, 61806206), Six Talent Peaks Project in Jiangsu Province (Grant Nos. 2015-DZXX-010, 2018-XYDXX-044), Natural Science Foundation of Jiangsu Province (Grant Nos. BK20180639, BK20171192, BK20180174), China Postdoctoral Science Foundation (Grant No. 2018M642359) and Innovative Research Group Project of the National Natural Science Foundation of China (Grant No. 61801198).
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Zhang, M., Zhou, Y., Zhao, J. et al. A survey of semi- and weakly supervised semantic segmentation of images. Artif Intell Rev 53, 4259–4288 (2020). https://doi.org/10.1007/s10462-019-09792-7
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DOI: https://doi.org/10.1007/s10462-019-09792-7