论文标题
部分可观测时空混沌系统的无模型预测
Weakly Supervised Scene Text Detection using Deep Reinforcement Learning
论文作者
论文摘要
场景文本检测的具有挑战性的领域需要复杂的数据注释，这既耗时又昂贵。诸如弱监督等技术可以减少所需的数据量。在本文中，我们提出了一种用于场景文本检测的薄弱监督方法，该方法利用了增强学习（RL）。 RL代理收到的奖励是由神经网络估算的，而不是从地面标签中推断出来。首先，我们通过几种培训优化增强了现有的监督RL文本检测方法，使我们能够缩小基于回归的算法的性能差距。然后，我们在对现实世界数据的弱和半监督培训中使用建议的系统。我们的结果表明，在弱监督环境中进行培训是可行的。但是，我们发现在半监督设置中使用我们的模型，例如当将标记的合成数据与未注释的现实世界数据相结合时，会产生最佳结果。
The challenging field of scene text detection requires complex data annotation, which is time-consuming and expensive. Techniques, such as weak supervision, can reduce the amount of data needed. In this paper we propose a weak supervision method for scene text detection, which makes use of reinforcement learning (RL). The reward received by the RL agent is estimated by a neural network, instead of being inferred from ground-truth labels. First, we enhance an existing supervised RL approach to text detection with several training optimizations, allowing us to close the performance gap to regression-based algorithms. We then use our proposed system in a weakly- and semi-supervised training on real-world data. Our results show that training in a weakly supervised setting is feasible. However, we find that using our model in a semi-supervised setting , e.g. when combining labeled synthetic data with unannotated real-world data, produces the best results.
