The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
Open accessAbstract
Emerging interests have been brought to recognize previously unseen objects given very few training examples, known as few-shot object detection (FSOD). Recent researches demonstrate that good feature embedding is the key to reach favorable few-shot learning performance. We observe object proposals with different Intersection-of-Union (IoU) scores are analogous to the intra-image augmentation used in contrastive visual representation learning. And we exploit this analogy and incorporate supervised contrastive learning to achieve more robust objects representations in FSOD. We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects. We notice the degradation of average precision (AP) for rare objects mainly comes from misclassifying novel instances as confusable classes. And we ease the misclassification issues by promoting instance level intraclass compactness and inter-class variance via our contrastive proposal encoding loss (CPE loss). Our design outperforms current state-of-the-art works in any shot and all data splits, with up to +8.8% on standard benchmark PASCAL VOC and +2.7% on challenging COCO benchmark. Code is available at: https://github.com/MegviiDetection/FSCE.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
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Microsoft COCO: Common Objects in Context
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Cited by
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
Song, Y., Wang, T., Cai, P., Mondal, S. K., & Sahoo, J. P. (2023) · ACM Computing Surveys · doi:10.1145/3582688 · Cited by 721
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning
Dong, N., Zhang, Y., Ding, M., & Lee, G. H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25129 · Cited by 62
Interpretable deep learning for improving cancer patient survival based on personal transcriptomes
(2023) · doi:10.1038/s41598-023-38429-7 · Cited by 13
Metadata
- Authors
- Sun, B., Li, B., Cai, S., Yuan, Y., & Zhang, C.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 7348-7358
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3169708801
- Cited by
- 555
- References
- 50
- FWCI
- 42.33
- Type
- conference paper
- Language
- en
- Open access
- green
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