Research Area:  Machine Learning
Big data-driven deep learning methods have been widely used in image or video segmentation. However, in practical applications, training a deep learning model requires a large amount of labeled data, which is difficult to achieve. Meta-learning, as one of the most promising research areas in the field of artificial intelligence, is believed to be a key tool for approaching artificial general intelligence. Compared with the traditional deep learning algorithm, meta-learning can update the learning task quickly and complete the corresponding learning with less data. To the best of our knowledge, there exist few researches in the meta-learning-based visual segmentation. To this end, this paper summarizes the algorithms and current situation of image or video segmentation technologies based on meta-learning and point out the future trends of meta-learning. Meta-learning has the characteristics of segmentation that based on semi-supervised or unsupervised learning, all the recent novel methods are summarized in this paper. The principle, advantages and disadvantages of each algorithms are also compared and analyzed.
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Author(s) Name:  Jiaxing Sun, Yujie Li
Journal name:  Cognitive Robotics
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Publisher name:  Elsevier
DOI:  10.1016/j.cogr.2021.06.003
Volume Information:  Volume 1, 2021, Pages 83-91
Paper Link:   https://www.sciencedirect.com/science/article/pii/S2667241321000070