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A survey of semi- and weakly supervised semantic segmentation of images

机译：关于图像的半和弱监督语义分割的调查

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摘要

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.
机译：图像语义分割是计算机愿景领域中最重要的任务之一，它在许多应用中取得了很大进展。许多完全监督的深度学习模型旨在实现复杂的语义分割任务，实验结果是显着的。然而，在完全监督学习中获取像素级标签是耗时和艰苦的，半监督和弱势监督的学习逐渐取代完全监督的学习，从而以较低的成本实现了良好的结果。基于诸如卷积神经网络，完全卷积网络，生成的对抗网络等常用模型，本文重点介绍了近年来核心方法和评论半和弱监督的语义分割模型。在以下章节中，详细概述了现有的评估和数据集，并根据数据集进行分析实验结果。本文的最后一部分是客观摘要。此外，它还指出了未来工作的研究和鼓舞人心的建议方向。

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