The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
VarifocalNet: An IoU-aware Dense Object Detector
Open accessAbstract
Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-Aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by ~2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors. Code is available at: https://github.com/hyz-xmaster/VarifocalNet.
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Cites
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Cited by
YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
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YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
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BackgroundMethodInfluentialTOOD: Task-aligned One-stage Object Detection
Feng, C., Zhong, Y., Gao, Y., Scott, M. R., & Huang, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00349 · Cited by 1,668
“0 VFNet [29]† CVPR21 ResNeXt-101-64×4d-DCN 49.”
Metadata
- Authors
- Zhang, H., Wang, Y., Dayoub, F., & Sunderhauf, N.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 8510-8519
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3172087149
- Cited by
- 1,142
- References
- 25
- FWCI
- 61.51
- Type
- conference paper
- Language
- en
- Open access
- green
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