用奖励机制训练分割模型，无需像素标注也能高效收敛
Semantic segmentation with reward
- 引入像素与图像级双重奖励，用强化学习优化分割网络
- 在图像级奖励下仍能收敛，性能超越现有弱监督方法
- 适合无精细标注、仅有质量反馈的现实场景
在真实场景中，像素级标注往往不可得。为解决此问题，本文提出RSS（Reward in Semantic Segmentation），首个在纯语义分割任务中应用奖励驱动强化学习的方法，支持像素级与图像级两种粒度的奖励。RSS融合渐进尺度奖励（PSR）和成对空间差异（PSD）等新技术，确保奖励信号有效促进网络收敛，尤其在图像级奖励条件下表现优异。在基准数据集上的实验与可视化表明，该方法在两类奖励下均能成功实现网络收敛。此外，仅使用图像级奖励的RSS，在性能上优于现有仅依赖图像级信号的弱监督方法。
原文摘要 · Abstract (English)
In real-world scenarios, pixel-level labeling is not always available. Sometimes, we need a semantic segmentation network, and even a visual encoder can have a high compatibility, and can be trained using various types of feedback beyond traditional labels, such as feedback that indicates the quality of the parsing results. To tackle this issue, we proposed RSS (Reward in Semantic Segmentation), the first practical application of reward-based reinforcement learning on pure semantic segmentation offered in two granular levels (pixel-level and image-level). RSS incorporates various novel technologies, such as progressive scale rewards (PSR) and pair-wise spatial difference (PSD), to ensure that the reward facilitates the convergence of the semantic segmentation network, especially under image-level rewards. Experiments and visualizations on benchmark datasets demonstrate that the proposed RSS can successfully ensure the convergence of the semantic segmentation network on two levels of rewards. Additionally, the RSS, which utilizes an image-level reward, outperforms existing weakly supervised methods that also rely solely on image-level signals during training.
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