PPGN: Phrase-Guided Proposal Generation Network For Referring Expression Comprehension
This work provides an incremental improvement for researchers working on two-stage referring expression comprehension methods by improving proposal quality.
This paper addresses the problem of low-quality proposal generation in referring expression comprehension (REC) by proposing a novel phrase-guided proposal generation network (PPGN). The method refines visual features using text to generate proposals through regression, achieving state-of-the-art performance on benchmark datasets.
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.