# CPARR: Category-based Proposal Analysis for Referring Relationships

arXiv: https://arxiv.org/abs/2004.08028 · HTML: https://mlsanity.com/paper/2004.08028/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Towards Flexible Visual Relationship Segmentation](https://mlsanity.com/paper/2408.08305/) (2024)
- [Grounding Scene Graphs on Natural Images via Visio-Lingual Message Passing](https://mlsanity.com/paper/2211.01969/) (2022)

## Abstract (arXiv)

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.