The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Perceive Where to Focus: Learning Visibility-Aware Part-Level Features for Partial Person Re-Identification
Open accessAbstract
This paper considers a realistic problem in person re-identification (re-ID) task, i.e., partial re-ID. Under partial re-ID scenario, the images may contain a partial observation of a pedestrian. If we directly compare a partial pedestrian image with a holistic one, the extreme spatial misalignment significantly compromises the discriminative ability of the learned representation. We propose a Visibility-aware Part Model (VPM) for partial re-ID, which learns to perceive the visibility of regions through self-supervision. The visibility awareness allows VPM to extract region-level features and compare two images with focus on their shared regions (which are visible on both images). VPM gains two-fold benefit toward higher accuracy for partial re-ID. On the one hand, compared with learning a global feature, VPM learns region-level features and thus benefits from fine-grained information. On the other hand, with visibility awareness, VPM is capable to estimate the shared regions between two images and thus suppresses the spatial misalignment. Experimental results confirm that our method significantly improves the learned feature representation and the achieved accuracy is on par with the state of the art.
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Cites
Deep Residual Learning for Image Recognition
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Self-citationFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Self-citationFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Self-citationFully convolutional networks for semantic segmentation
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Self-citationRealtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539 · cited 1×
“[23] extract several region parts, with pose estimation [17, 27, 10, 18, 1], human parsing [2, 5] and uniform partitioning, respectively.”
BackgroundStacked Hourglass Networks for Human Pose Estimation
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Scalable Person Re-identification: A Benchmark
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Cited by
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-identification
Ye, M., Shen, J., J. Crandall, D., Shao, L., & Luo, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58520-4_14 · Cited by 523
High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
Wang, G., Yang, S., Liu, H., Wang, Z., Yang, Y., Wang, S., Yu, G., Zhou, E., & Sun, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00648 · Cited by 512
Self-citationInfrared-Visible Cross-Modal Person Re-Identification with an X Modality
Li, D., Wei, X., Hong, X., & Gong, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5891 · Cited by 431
Visible-Infrared Person Re-Identification via Homogeneous Augmented Tri-Modal Learning
Ye, M., Shen, J., & Shao, L. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3001665 · Cited by 305
Salience-Guided Cascaded Suppression Network for Person Re-Identification
Chen, X., Fu, C., Zhao, Y., Zheng, F., Song, J., Ji, R., & Yang, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00336 · Cited by 266
Metadata
- Authors
- Sun, Y., Xu, Q., Li, Y., Zhang, C., Li, Y., Wang, S., & Sun, J.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 393-402
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2931208564
- Cited by
- 388
- References
- 40
- FWCI
- 19.40
- Type
- conference paper
- Language
- en
- Open access
- green
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- Sun, Y., Xu, Q., Li, Y., Zhang, C., Li, Y., Wang, S., & Sun, J. (2019). Perceive Where to Focus: Learning Visibility-Aware Part-Level Features for Partial Person Re-Identification. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00048
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