Visual grounding aims to localize an object in an image referred to by a textual query phrase. Various visual grounding approaches have been proposed, and the problem can be modularized into a general framework: proposal generation, multi-modal feature representation, and proposal ranking. Of these three modules, most existing approaches focus on the latter two, with the importance of proposal generation generally neglected. In this paper, we rethink the problem of what properties make a good proposal generator. We introduce the diversity and discrimination simultaneously when generating proposals, and in doing so propose Diversified and Discriminative Proposal Networks model (DDPN). Based on the proposals generated by DDPN, we propose a high performance baseline model for visual grounding and evaluate it on four benchmark datasets. Experimental results demonstrate that our model delivers significant improvements on all the tested data-sets (e.g., 18.8\% improvement on ReferItGame and 8.2\% improvement on Flickr30k Entities over the existing state-of-the-arts respectively)

本文介绍了Diversified and Discriminative Proposal Networks模型（DDPN），并在此基础上提出了一个高性能的视觉定位基线模型，通过在四个基准数据集上进行评估，证明了该模型在所有测试数据集上都有显着改善。

重构用于视觉 grounding 的多样化和区分度建议生成