  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.
