Deep Learning for Visual Segmentation: A Review

Jiaxing Sun, Yujie Li, Huimin Lu, Tohru Kamiya, S. Serikawa
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引用次数: 2

Abstract

Big data-driven deep learning methods have been widely used in image or video segmentation. The main challenge is that a large amount of labeled data is required in training deep learning models, which is important in real-world applications. To the best of our knowledge, there exist few researches in the deep learning-based visual segmentation. To this end, this paper summarizes the algorithms and current situation of image or video segmentation technologies based on deep learning and point out the future trends. The characteristics of segmentation that based on semi-supervised or unsupervised learning, all of 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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深度学习用于视觉分割:综述
大数据驱动的深度学习方法已广泛应用于图像或视频分割。主要的挑战是在训练深度学习模型时需要大量的标记数据,这在实际应用中很重要。据我们所知,基于深度学习的视觉分割研究很少。为此,本文总结了基于深度学习的图像或视频分割技术的算法和现状,并指出了未来的发展趋势。本文总结了基于半监督学习和无监督学习的分割方法的特点,以及近年来各种新的分割方法。对各种算法的原理、优缺点进行了比较和分析。
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