论文标题
一个统一的相互监督框架，用于引用表达细分和生成
A Unified Mutual Supervision Framework for Referring Expression Segmentation and Generation
论文作者
论文摘要
参考表达分割（RES）和参考表达生成（REG）是可以自然训练的相互反向任务。尽管最近的工作探讨了这种联合培训，但是RES和REG如何互相受益的机制仍不清楚。在本文中，我们提出了一个统一的相互监督框架，使两个任务相互改进。我们的相互监督包含两个方向。一方面，放弃歧义监督利用RES提供的表达不合格的测量来增强REG的语言生成。另一方面，生成监督使用Reg自动生成的表达式来扩展RES的培训。这种统一的相互监督通过解决瓶颈问题有效地改善了两个任务。广泛的实验表明，我们的方法在同一设置下的REG和RES任务上的所有现有方法都大大优于所有现有方法，并且详细的消融研究证明了所有组件在我们的框架中的有效性。
Reference Expression Segmentation (RES) and Reference Expression Generation (REG) are mutually inverse tasks that can be naturally jointly trained. Though recent work has explored such joint training, the mechanism of how RES and REG can benefit each other is still unclear. In this paper, we propose a unified mutual supervision framework that enables two tasks to improve each other. Our mutual supervision contains two directions. On the one hand, Disambiguation Supervision leverages the expression unambiguity measurement provided by RES to enhance the language generation of REG. On the other hand, Generation Supervision uses expressions automatically generated by REG to scale up the training of RES. Such unified mutual supervision effectively improves two tasks by solving their bottleneck problems. Extensive experiments show that our approach significantly outperforms all existing methods on REG and RES tasks under the same setting, and detailed ablation studies demonstrate the effectiveness of all components in our framework.
