Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN

Author:

Xu Hang,Fang Linpu,Liang Xiaodan,Kang Wenxiong,Li Zhenguo

Abstract

The dominant object detection approaches treat each dataset separately and fit towards a specific domain, which cannot adapt to other domains without extensive retraining. In this paper, we address the problem of designing a universal object detection model that exploits diverse category granularity from multiple domains and predict all kinds of categories in one system. Existing works treat this problem by integrating multiple detection branches upon one shared backbone network. However, this paradigm overlooks the crucial semantic correlations between multiple domains, such as categories hierarchy, visual similarity, and linguistic relationship. To address these drawbacks, we present a novel universal object detector called Universal-RCNN that incorporates graph transfer learning for propagating relevant semantic information across multiple datasets to reach semantic coherency. Specifically, we first generate a global semantic pool by integrating all high-level semantic representation of all the categories. Then an Intra-Domain Reasoning Module learns and propagates the sparse graph representation within one dataset guided by a spatial-aware GCN. Finally, an Inter-Domain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally. Extensive experiments demonstrate that the proposed method significantly outperforms multiple-branch models and achieves the state-of-the-art results on multiple object detection benchmarks (mAP: 49.1% on COCO).

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Universal Object Detection with Large Vision Model;International Journal of Computer Vision;2023-11-07

2. Detecting Objects with Context-Likelihood Graphs and Graph Refinement;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

3. ScaleDet: A Scalable Multi-Dataset Object Detector;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

4. Graph Representation Learning Meets Computer Vision: A Survey;IEEE Transactions on Artificial Intelligence;2023-02

5. A Method of Fusing Probability-Form Knowledge into Object Detection in Remote Sensing Images;Remote Sensing;2022-12-01

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