Reference expression comprehension (REC) aims to find the location that the phrase refer to in a given image. Proposal generation and proposal representation are two effective techniques in many two-stage REC methods. However, most of the existing works only focus on proposal representation and neglect the importance of proposal generation. As a result, the low-quality proposals generated by these methods become the performance bottleneck in REC tasks. In this paper, we reconsider the problem of proposal generation, and propose a novel phrase-guided proposal generation network (PPGN). The main implementation principle of PPGN is refining visual features with text and generate proposals through regression. Experiments show that our method is effective and achieve SOTA performance in benchmark datasets.

中文翻译：

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PPGN：用于指导表达理解的短语指导的提案生成网络

参考表达理解（REC）的目的是找到短语在给定图像中所指的位置。提案生成和提案表示是许多两阶段REC方法中的两种有效技术。但是，大多数现有作品仅关注提案表示，而忽略了提案生成的重要性。结果，这些方法生成的低质量建议成为REC任务中的性能瓶颈。在本文中，我们重新考虑了提案生成问题，并提出了一种新颖的词组引导提案生成网络（PPGN）。PPGN的主要实现原理是用文本完善视觉功能并通过回归生成建议。实验表明，该方法是有效的，并在基准数据集中实现了SOTA性能。