Abstract In computer vision, the research community has been looking to how to benefit from weakly supervised learning that utilizes easily obtained image-level labels to train neural network models. The existing deep convolutional neural networks for weakly supervised learning, however, generally do not fully exploit the label dependencies in an image. To make full use of this information, in this paper, we propose a new framework for weakly supervised learning of deep convolutional neural networks, introducing graph convolutional networks to capture the semantic label co-occurrence in an image. Moreover, we propose a novel initialization method for label embedding in graph convolutional networks, which enables a smoother optimization for interrelationships learning. Extensive experiments and comparisons on four public benchmark datasets (PASCAL VOC 2007, PASCAL VOC 2012, Microsoft COCO, and NUS-WIDE) show the superior performance of our approach in both image classification and weakly supervised pointwise object localization. These results lead us to conclude that the label dependencies in the input image can provide valuable evidence for learning strongly localized features.

中文翻译：

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使用图卷积网络进行弱监督图像分类和逐点定位

摘要 在计算机视觉中，研究界一直在寻找如何从弱监督学习中受益，弱监督学习利用容易获得的图像级标签来训练神经网络模型。然而，现有的用于弱监督学习的深度卷积神经网络通常没有充分利用图像中的标签依赖性。为了充分利用这些信息，在本文中，我们提出了一种新的深度卷积神经网络弱监督学习框架，引入图卷积网络来捕获图像中的语义标签共现。此外，我们提出了一种新的用于图卷积网络中标签嵌入的初始化方法，它可以对相互关系学习进行更平滑的优化。对四个公共基准数据集（PASCAL VOC 2007、PASCAL VOC 2012、Microsoft COCO 和 NUS-WIDE）的大量实验和比较表明，我们的方法在图像分类和弱监督逐点对象定位方面的卓越性能。这些结果使我们得出结论，输入图像中的标签依赖性可以为学习强局部特征提供有价值的证据。