The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector
Open accessAbstract
Conventional methods for object detection typically require a substantial amount of training data and preparing such high-quality training data is very labor-intensive. In this paper, we propose a novel few-shot object detection network that aims at detecting objects of unseen categories with only a few annotated examples. Central to our method are our Attention-RPN, Multi-Relation Detector and Contrastive Training strategy, which exploit the similarity between the few shot support set and query set to detect novel objects while suppressing false detection in the background. To train our network, we contribute a new dataset that contains 1000 categories of various objects with high-quality annotations. To the best of our knowledge, this is one of the first datasets specifically designed for few-shot object detection. Once our few-shot network is trained, it can detect objects of unseen categories without further training or fine-tuning. Our method is general and has a wide range of potential applications. We produce a new state-of-the-art performance on different datasets in the few-shot setting. The dataset link is https://github.com/fanq15/Few-Shot-Object-Detection-Dataset.
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Cited by
Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
Sun, B., Li, B., Cai, S., Yuan, Y., & Zhang, C. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00727 · Cited by 555
Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection
Zhang, G., Cui, K., Hung, T.-Y., & Lu, S. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00257 · Cited by 200
Few-Shot Object Detection: A Survey
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Meta-Learning-Based Incremental Few-Shot Object Detection
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Metadata
- Authors
- Fan, Q., Zhuo, W., Tang, C.-K., & Tai, Y.-W.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 4012-4021
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034974675
- Cited by
- 632
- References
- 66
- FWCI
- 37.49
- Type
- conference paper
- Language
- en
- Open access
- green
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- Fan, Q., Zhuo, W., Tang, C.-K., & Tai, Y.-W. (2020). Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00407
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