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首页> 外文期刊>IEEE Signal Processing Magazine >Weakly Supervised Learning with Deep Convolutional Neural Networks for Semantic Segmentation: Understanding Semantic Layout of Images with Minimum Human Supervision
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Weakly Supervised Learning with Deep Convolutional Neural Networks for Semantic Segmentation: Understanding Semantic Layout of Images with Minimum Human Supervision

机译：使用深度卷积神经网络进行语义监督的弱监督学习：以最少的人工监督了解图像的语义布局

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摘要

Semantic segmentation is a popular visual recognition task whose goal is to estimate pixel-level object class labels in images. This problem has been recently handled by deep convolutional neural networks (DCNNs), and the state-of-theart techniques achieve impressive records on public benchmark data sets. However, learning DCNNs demand a large number of annotated training data while segmentation annotations in existing data sets are significantly limited in terms of both quantity and diversity due to the heavy annotation cost. Weakly supervised approaches tackle this issue by leveraging weak annotations such as image-level labels and bounding boxes, which are either readily available in existing large-scale data sets for image classification and object detection or easily obtained thanks to their low annotation costs. The main challenge in weakly supervised semantic segmentation then is the incomplete annotations that miss accurate object boundary information required to learn segmentation. This article provides a comprehensive overview of weakly supervised approaches for semantic segmentation. Specifically, we describe how the approaches overcome the limitations and discuss research directions worthy of investigation to improve performance.
机译：语义分割是一种流行的视觉识别任务，其目标是估计图像中的像素级对象类别标签。最近，深度卷积神经网络（DCNN）解决了这个问题，并且最新技术在公共基准数据集上取得了令人印象深刻的记录。但是，学习型DCNN需要大量带注释的训练数据，而现有数据集中的分段注释在数量和多样性方面都受到很大的限制，这是因为注释成本很高。弱监督的方法通过利用弱注释（例如图像级标签和边界框）来解决此问题，这些注释可以在现有的大规模数据集中轻松用于图像分类和对象检测，或者由于注释成本低而容易获得。那么，在弱监督语义分割中的主要挑战是不完整的注释会丢失学习分割所需的准确对象边界信息。本文提供了语义监督的弱监督方法的全面概述。具体来说，我们描述了这些方法如何克服局限性，并讨论了值得改进性能的研究方向。

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