# OD-GCN: Object Detection Boosted by Knowledge GCN

Source: [OD-GCN: Object Detection Boosted by Knowledge GCN](<https://www.sota2.com/research/paper/68fa66af1e209131cea11c7f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:56:21.324Z

Authors: Zheng Liu, Zidong Jiang, Wei Feng, Hui Feng

Published: 2019-08-06T02:23:29.000Z

## Abstract

Classical CNN based object detection methods only extract the objects' image features, but do not consider the high-level relationship among objects in context. In this article, the graph convolutional networks (GCN) is integrated into the object detection framework to exploit the benefit of category relationship among objects, which is able to provide extra confidence for any pre-trained object detection model in our framework. In experiments, we test several popular base detection models on COCO dataset. The results show promising improvement on mAP by 1-5pp. In addition, visualized analysis reveals the benchmark improvement is quite reasonable in human's opinion.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1908.04385>)

[PDF](<https://arxiv.org/pdf/1908.04385.pdf>)