Cross-Domain Detection via Graph-Induced Prototype Alignment

@article{Xu2020CrossDomainDV,
  title={Cross-Domain Detection via Graph-Induced Prototype Alignment},
  author={Minghao Xu and Hang Wang and Bingbing Ni and Qi Tian and Wenjun Zhang},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={12352-12361}
}
  • Minghao XuHang Wang Wenjun Zhang
  • Published 28 March 2020
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Applying the knowledge of an object detector trained on a specific domain directly onto a new domain is risky, as the gap between two domains can severely degrade model's performance. Furthermore, since different instances commonly embody distinct modal information in object detection scenario, the feature alignment of source and target domain is hard to be realized. To mitigate these problems, we propose a Graph-induced Prototype Alignment (GPA) framework to seek for category-level domain… 
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115 Citations
Highly Influential Citations
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115 Citations

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