The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning
Abstract
Resembling the rapid learning capability of human, low-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived from meta-learning on images with a single visual object. Obfuscated by a complex background and multiple objects in one image, they are hard to promote the research of low-shot object detection/segmentation. In this work, we present aflexible and general methodology to achieve these tasks. Our work extends Faster /Mask R-CNN by proposing meta-learning over RoI (Region-of-Interest) features instead of a full image feature. This simple spirit disentangles multi-object information merged with the background, without bells and whistles, enabling Faster /Mask R-CNN turn into a meta-learner to achieve the tasks. Specifically, we introduce a Predictor-head Remodeling Network (PRN) that shares its main backbone with Faster /Mask R-CNN. PRN receives images containing low-shot objects with their bounding boxes or masks to infer their class attentive vectors. The vectors take channel-wise soft-attention on RoI features, remodeling those R-CNN predictor heads to detect or segment the objects consistent with the classes these vectors represent. In our experiments, Meta R-CNN yields the new state of the art in low-shot object detection and improves low-shot object segmentation byMaskR-CNN.Code: https://yanxp.github.io/metarcnn.html.
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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
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You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Microsoft COCO: Common Objects in Context
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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
Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector
Fan, Q., Zhuo, W., Tang, C.-K., & Tai, Y.-W. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00407 · Cited by 632
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
Antonelli, S., Avola, D., Cinque, L., Crisostomi, D., Foresti, G. L., Galasso, F., Marini, M. R., Mecca, A., & Pannone, D. (2022) · ACM Computing Surveys · doi:10.1145/3519022 · Cited by 121
Metadata
- Authors
- Yan, X., Chen, Z., Xu, A., Wang, X., Liang, X., & Lin, L.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9576-9585
- OpenAlex
- W2996583130
- Cited by
- 616
- References
- 46
- FWCI
- 20.63
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yan, X., Chen, Z., Xu, A., Wang, X., Liang, X., & Lin, L. (2019). Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00967
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