CPARR: Category-Based Proposal Analysis for Referring Relationships

Chuanzi He, Haidong Zhu, Jiyang Gao, Kan Chen, Ram Nevatia; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 948-949

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 . 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.

Related Material

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[bibtex]
@InProceedings{He_2020_CVPR_Workshops,
author = {He, Chuanzi and Zhu, Haidong and Gao, Jiyang and Chen, Kan and Nevatia, Ram},
title = {CPARR: Category-Based Proposal Analysis for Referring Relationships},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}