论文标题
CPARR：基于类别的建议分析用于推荐关系
CPARR: Category-based Proposal Analysis for Referring Relationships
论文作者
论文摘要
引用关系的任务是在满足关系查询的图像中本地化主题和对象实体，该图像以\ texttt {<主题，谓词，object>}的形式给出。这需要在指定关系中同时将主题和对象实体定位。我们介绍了一种简单但有效的基于提案的方法来引用关系。与现有方法（例如SSA）不同，我们的方法可以在降低其复杂性和模棱两可的同时产生高分辨率结果。我们的方法由两个模块组成：一个基于类别的提案生成模块，以选择与实体相关的建议和一个谓词分析模块，以评分成对的成对提案的兼容性。我们在两个公共数据集上的参考关系任务上显示了最新的性能：视觉关系检测和视觉基因组。
The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of \texttt{<subject, predicate, object>}. This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple yet effective proposal-based method for referring relationships. Different from the existing methods such as SSAS, our method can generate a high-resolution result while reducing its complexity and ambiguity. Our method is composed of two modules: a category-based proposal generation module to select the proposals related to the entities and a predicate analysis module to score the compatibility of pairs of selected proposals. We show state-of-the-art performance on the referring relationship task on two public datasets: Visual Relationship Detection and Visual Genome.
