Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

CPARR: Category-based Proposal Analysis for Referring Relationships (2004.08028v1)

Published 17 Apr 2020 in cs.CV

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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Chuanzi He (1 paper)
  2. Haidong Zhu (15 papers)
  3. Jiyang Gao (28 papers)
  4. Kan Chen (74 papers)
  5. Ram Nevatia (54 papers)
Citations (6)

Summary

We haven't generated a summary for this paper yet.