The scarcity of fully-annotated data becomes the biggest obstacle that prevents many deep learning approaches from widely applied. Weakly-supervised visual learning which can utilize inexact annotations is developed rapidly to remedy such a situation. In this paper, we study the weakly-supervised task achieving pixel-level semantic segmentation only with image-level labels as supervision. Different from other methods, our approach tries to transform the weakly-supervised visual learning problem into a semi-supervised visual learning problem and then utilizes semi-supervised learning methods to solve it. Utilizing this transformation, we can adopt effective semi-supervised methods to perform transductive learning with context information. In the semi-supervised learning module, we propose to use the graph cut algorithm to label more supervision from the activation seeds generated from a classification network. The generated labels can provide the segmentation model with effective supervision information; moreover, the graph cut module can benefit from features extracted by the segmentation model. Then, each of them updates and optimizes the other iteratively until convergence. Experiment results on PASCAL VOC and COCO benchmarks demonstrate the effectiveness of the proposed deep graph cut algorithm for weakly-supervised semantic segmentation.

完全注释数据的稀缺性成为阻碍许多深度学习方法广泛应用的最大障碍。可以利用不精确注释的弱监督视觉学习得到了迅速发展，以纠正这种情况。在本文中，我们研究仅在图像级标签作为监督的情况下实现像素级语义分割的弱监督任务。与其他方法不同，我们的方法试图将弱监督的视觉学习问题转化为半监督的视觉学习问题，然后利用半监督的学习方法来解决它。利用这种转换，我们可以采用有效的半监督方法对上下文信息进行跨语言学习。在半监督学习模块中，我们建议使用图割算法从分类网络生成的激活种子中标记更多监督。生成的标签可以为分割模型提供有效的监督信息。此外，图切割模块可以受益于分割模型提取的特征。然后，它们每个都迭代地更新和优化另一个，直到收敛为止。在PASCAL VOC和COCO基准测试中的实验结果证明了所提出的深度图割算法对于弱监督语义分割的有效性。它们中的每一个迭代地更新和优化另一个，直到收敛为止。在PASCAL VOC和COCO基准测试中的实验结果证明了所提出的深度图割算法对于弱监督语义分割的有效性。它们中的每一个迭代地更新和优化另一个，直到收敛为止。在PASCAL VOC和COCO基准测试中的实验结果证明了所提出的深度图割算法对于弱监督语义分割的有效性。