Improving Object Detection with Relation Graph Inference

Chen Hang He, Shun Cheung Lai, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Many classic object detection approaches have proven that detection performance can be improved by adding the object's context information. However, only a few methods have attempted to exploit the object-to-object relationship during learning. The reason for this is that objects may appear at different locations in an image, with an arbitrary size and scale. This makes it difficult to model the objects in a unified way within a network. Inspired by Graph Convolutional Network (GCN), we propose a detection algorithm that can infer the relationship among multiple objects during the inference, achieved by constructing a relation graph dynamically with a self-adopted attention mechanism. The relation graph encodes both the geometric and visual relationship between objects. This can enrich the object feature by aggregating the information from the object and its relevant neighbors. Experiments show that our proposed module can efficiently improve the detection performance of existing object detectors.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2537-2541
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - 12 May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • Graph convolutional neural network
  • Object detection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this