Weakly supervised referring expression grounding aims at localizing the referential object in an image according to the linguistic query, where the mapping between the referential object and query is unknown in the training stage. To address this problem, we propose a novel end-to-end adaptive reconstruction network (ARN). It builds the correspondence between image region proposal and query in an adaptive manner: adaptive grounding and collaborative reconstruction. Specifically, we first extract the subject, location and context features to represent the proposals and the query respectively. Then, we design the adaptive grounding module to compute the matching score between each proposal and query by a hierarchical attention model. Finally, based on attention score and proposal features, we reconstruct the input query with a collaborative loss of language reconstruction loss, adaptive reconstruction loss, and attribute classification loss. This adaptive mechanism helps our model to alleviate the variance of different referring expressions. Experiments on four large-scale datasets show ARN outperforms existing state-of-the-art methods by a large margin. Qualitative results demonstrate that the proposed ARN can better handle the situation where multiple objects of a particular category situated together.

本文提出了一种使用自适应重建网络(ARN)解决弱监督引用表达地面问题的方法，该模型使用分层注意模型计算每个提案和查询之间的匹配分数，并采用语言重建损失、自适应重建损失和属性分类损失协同重建输入查询，实验证明ARN比现有的最新方法在四个大型数据集上表现更好，并能更好地处理特定类别的多个对象位于一起的情况。

弱监督指代消解的自适应重建网络