OD-GCN object detection by knowledge graph with GCN

08/06/2019 ∙ by Zheng Liu, et al. ∙ 11

Classical object detection frameworks lack of utilizing objects' surrounding information. In this article, we introduce the graph convolutional networks (GCN) into the object detection, and propose a new framework called OD-GCN (object detection with graph convolutional network). It utilizes the category relationship to improve the detection precision. We set up a knowledge graph to reflect the co-exist relationships among objects. GCN plays the role of post-processing to adjust the output of base object detection models. It is a flexible framework that any pre-trained object detection models can be used as the base model. In the experiments, we try several popular base detection models, OD-GCN always improve mAP by 1-5 pp in COCO dataset. In addition, visualized analysis reveals the benchmark improvement is quite logical in human's opinion.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

page 5

page 6

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.