End-to-end weakly supervised semantic segmentation aims at optimizing a segmentation model in a single-stage training process based on only image annotations. Existing methods adopt an online-trained classification branch to provide pseudo annotations for supervising the segmentation branch. However, this strategy makes the classification branch dominate the whole concurrent training process, hindering these two branches from assisting each other. In our work, we treat these two branches equally by viewing them as diverse ways to generate the segmentation map, and add interactions on both their supervision and operation to achieve mutual promotion. For this purpose, a bidirectional supervision mechanism is elaborated to force the consistency between the outputs of these two branches. Thus, the segmentation branch can also give feedback to the classification branch to enhance the quality of localization seeds. Moreover, our method also designs interaction operations between these two branches to exchange their knowledge to assist each other. Experiments indicate our work outperforms existing end-to-end weakly supervised segmentation methods.

翻译：端到端弱监督语义分割旨在仅基于图像标注的单一阶段训练过程中优化分割模型。现有方法采用在线训练的分类分支来提供伪标注以监督分割分支。然而，这种策略使得分类分支主导整个并行训练过程，阻碍了两分支之间的相互协作。在本工作中，我们将这两个分支视为生成分割图的两种不同途径，通过在其监督和操作层面添加交互来实现相互促进。为此，我们精心设计了一种双向监督机制，以强制两分支输出之间的一致性。这样一来，分割分支也能向分类分支提供反馈，从而提升定位种子点的质量。此外，我们的方法还设计了这两分支之间的交互操作，以交换彼此知识并相互协助。实验表明，我们的工作优于现有的端到端弱监督分割方法。