The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
Open accessAbstract
We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped images, we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image. This allows us to address the problem of limited size of existing datasets for crowd counting. We collect two crowd scene datasets from Google using keyword searches and query-by-example image retrieval, respectively. We demonstrate how to efficiently learn from these unlabeled datasets by incorporating learning-to-rank in a multi-task network which simultaneously ranks images and estimates crowd density maps. Experiments on two of the most challenging crowd counting datasets show that our approach obtains state-of-the-art results.
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Cites
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Cited by
Learning From Synthetic Data for Crowd Counting in the Wild
Wang, Q., Gao, J., Lin, W., & Yuan, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00839 · Cited by 654
Attention Scaling for Crowd Counting
Jiang, X., Zhang, L., Xu, M., Zhang, T., Lv, P., Zhou, B., Yang, X., & Pang, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00476 · Cited by 325
PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
Gao, J., Wang, Q., & Li, X. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2919139 · Cited by 284
Guided attention network for object detection and counting on drones
YuanQiang, C., Du, D., Zhang, L., Wen, L., Wang, W., Wu, Y., & Lyu, S. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413816 · Cited by 58
Attention-Based Deep Bayesian Counting For AI-Augmented Agriculture
Wang, Y., Gu, M., Zhou, M., & Qian, X. (2022) · Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/3560905.3568417 · Cited by 5
Metadata
- Authors
- Liu, X., van de Weijer, J., & Bagdanov, A. D.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 7661-7669
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963231953
- Cited by
- 308
- References
- 39
- FWCI
- 15.46
- Type
- conference paper
- Language
- en
- Open access
- green
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