# OD-GCN: Object Detection Boosted by Knowledge GCN

arXiv: https://arxiv.org/abs/1908.04385 · HTML: https://mlsanity.com/paper/1908.04385/

## Key facts

- Cited by 4 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [RTIC: Residual Learning for Text and Image Composition using Graph Convolutional Network](https://mlsanity.com/paper/2104.03015/) (2021)
- [GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph](https://mlsanity.com/paper/2105.11852/) (2021)
- [Graph Neural Networks in Computer Vision -- Architectures, Datasets and Common Approaches](https://mlsanity.com/paper/2212.10207/) (2022)
- [I-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data](https://mlsanity.com/paper/2406.06239/) (2024)

## Abstract (arXiv)

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.