The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Rethinking Classification and Localization for Object Detection
Open accessAbstract
Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively.
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Cites
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Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., et al. (2019) · arXiv · doi:10.48550/arxiv.1906.07155 · Cited by 2,227
Self-citationFocal and efficient IOU loss for accurate bounding box regression
Zhang, Y.-F., Ren, W., Zhang, Z., Jia, Z., Wang, L., & Tan, T. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.07.042 · Cited by 2,039
Self-citationDetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
Qiao, S., Chen, L.-C., & Yuille, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01008 · Cited by 1,245
Metadata
- Authors
- Wu, Y., Chen, Y., Yuan, L., Liu, Z., Wang, L., Li, H., & Fu, Y.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 10183-10192
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035694605
- Cited by
- 712
- References
- 22
- FWCI
- 32.77
- Type
- conference paper
- Language
- en
- Open access
- green
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