CPARR: Category-based Proposal Analysis for Referring Relationships
Abstract
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.
Keywords
Cite
@article{arxiv.2004.08028,
title = {CPARR: Category-based Proposal Analysis for Referring Relationships},
author = {Chuanzi He and Haidong Zhu and Jiyang Gao and Kan Chen and Ram Nevatia},
journal= {arXiv preprint arXiv:2004.08028},
year = {2020}
}
Comments
CVPR 2020 Workshop on Multimodal Learning