Training a fully supervised semantic segmentation network requires a large amount of expensive pixel-level annotations in manual labor. In this work, we focus on studying the semantic segmentation problem using only image-level supervision. An effective scheme for weakly supervised segmentation is employed to produce the proxy annotations via image tags firstly. Then the segmentation network is retrained on the generated noisy proxy annotations. However, learning from noisy annotations is risky, as proxy annotations of poor quality may deteriorate the performance of the baseline segmentation and classification networks. In order to train the segmentation network using noisy annotations more effectively, two novel loss functions are proposed in this paper, namely, the selection loss and attention loss. Firstly, a selection loss is designed by weighting the proxy annotations based on a coarse-to-fine strategy for evaluating the quality of segmentation masks. Secondly, an attention loss taking the clean image tags as supervision is utilized to correct the classification errors caused by ambiguous pixel-level labels. Finally, we propose an end-to-end semantic segmentation network SAL-Net guided by the above two losses. From the extensive experiments conducted on PASCAL VOC 2012 dataset, SAL-Net reaches state-of-the-art performance with mean IoU (mIoU) as 62.5% and 66.6% on the test set by taking VGG16 network and ResNet101 network as the baselines respectively, which demonstrates the superiority of the proposed algorithm over eight representative weakly supervised segmentation methods. The code and models will be available at https://github.com/zmbhou/SALTMM.

中文翻译：

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SAL：弱监督语义分割的选择和注意力损失

训练一个完全监督的语义分割网络需要大量昂贵的手工劳动的像素级注释。在这项工作中，我们专注于仅使用图像级监督来研究语义分割问题。首先采用弱监督分割的有效方案通过图像标签生成代理注释。然后在生成的嘈杂代理注释上重新训练分割网络。然而，从嘈杂的注释中学习是有风险的，因为质量差的代理注释可能会降低基线分割和分类网络的性能。为了更有效地使用噪声标注训练分割网络，本文提出了两种新的损失函数，即选择损失和注意力损失。首先，通过基于从粗到细的策略对代理注释进行加权来设计选择损失，以评估分割掩码的质量。其次，利用以干净图像标签为监督的注意力损失来纠正由模糊像素级标签引起的分类错误。最后，我们提出了一个以上述两个损失为指导的端到端语义分割网络 SAL-Net。从在 PASCAL VOC 2012 数据集上进行的大量实验来看，SAL-Net 分别以 VGG16 网络和 ResNet101 网络为基线，在测试集上达到了最先进的性能，平均 IoU (mIoU) 分别为 62.5% 和 66.6% ，这证明了所提出的算法优于八种有代表性的弱监督分割方法。代码和模型将在 https://github 上提供。