Weakly supervised semantic segmentation under image-level annotations is effectiveness for real-world applications. The small and sparse discriminative regions obtained from an image classification network that are typically used as the important initial location of semantic segmentation also form the bottleneck. Although deep convolutional neural networks (DCNNs) have exhibited promising performances for single-label image classification tasks, images of the real-world usually contain multiple categories, which is still an open problem. So, the problem of obtaining high-confidence discriminative regions from multi-label classification networks remains unsolved. To solve this problem, this article proposes an innovative three-step framework within the perspective of multi-object proposal generation. First, an image is divided into candidate boxes using the object proposal method. The candidate boxes are sent to a single-classification network to obtain the discriminative regions. Second, the discriminative regions are aggregated to obtain a high-confidence seed map. Third, the seed cues grow on the feature maps of high-level semantics produced by a backbone segmentation network. Experiments are carried out on the PASCAL VOC 2012 dataset to verify the effectiveness of our approach, which is shown to outperform other baseline image segmentation methods.

中文翻译：

---

通过聚合种子线索的弱监督语义分割网络：多对象建议生成视角

图像级注释下的弱监督语义分割对于实际应用是有效的。从图像分类网络中获得的小而稀疏的判别区域通常用作语义分割的重要初始位置，也构成了瓶颈。尽管深度卷积神经网络 (DCNN) 在单标签图像分类任务中表现出可观的性能，但现实世界的图像通常包含多个类别，这仍然是一个悬而未决的问题。因此，从多标签分类网络中获得高置信度判别区域的问题仍未解决。为了解决这个问题，本文从多对象提案生成的角度提出了一个创新的三步框架。第一的，使用对象提议方法将图像划分为候选框。候选框被发送到单分类网络以获得判别区域。其次，将判别区域聚合以获得高置信度种子图。第三，种子线索在骨干分割网络产生的高级语义特征图上生长。在 PASCAL VOC 2012 数据集上进行了实验，以验证我们方法的有效性，该方法被证明优于其他基线图像分割方法。种子线索在骨干分割网络产生的高级语义特征图上增长。在 PASCAL VOC 2012 数据集上进行了实验，以验证我们方法的有效性，该方法被证明优于其他基线图像分割方法。种子线索在骨干分割网络产生的高级语义特征图上增长。在 PASCAL VOC 2012 数据集上进行了实验，以验证我们方法的有效性，该方法被证明优于其他基线图像分割方法。