Learning Rubost Features with Refined Spatial Interactions of Inter-part for Person Re-IDentification
Authors: 
Bo Tan, Meibin Qi, Cuiqun Chen, Fang Su, Jianguo JiangAuthors Info & Claims
Pages 153 - 156
Abstract
The combination of the part features and attention mechanism has been made remarkable progress in Person Re-IDentification (REID), but part-based approaches are still ignoring the relations of inter-part and missing discriminative local feature representations. The Refined Spatial Interactions of Inter-part for Person Re-IDentification (RSIPR) is proposed to solve above problems. (RSIPR) consists of two main components: 1) Self Filter Attention (SFA) mechanism is employed to extract discriminatory representations from the pedestrian image adaptively. 2) Refined Spatial Interactions (RSI) is utilized to refine the interactions of inter-part and avoid ignoring the global perception due to the pedestrain is cropped into patches directly. Extensive experiments have been implemented to validate the superiority of the (RSIPR), and it has been demonstrated that the (RSIPR) achieves the state-of-the-art method without re-ranking [1] algorithm.
References
[1]
Zhun Zhong, Liang Zheng, Donglin Cao, and Shaozi Li. Re-ranking person re-identification with k-reciprocal encoding. In CVPR, 2017.B. Xiao, H. Wu, and Y. Wei. Simple baselines for human pose estimation and tracking. In ECCV, 2018.
[2]
Fu, Y., Wei, Y., Zhou, Y., Shi, H., Huang, G., Wang, X., Yao, Z., Huang, and T.S. Horizontal pyramid matching for person re-identification. CoRR abs/1804.05275, 2018.
[3]
Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. Beyond part models: Person retrieval with refined part pooling. In ECCV, 2018.
[4]
Guanshuo Wang, Yufeng Yuan, Xiong Chen, Jiwei Li, and Xi Zhou. Learning discriminative features with multiple granularities for person re-identification. In ACM, 2018.
[5]
B. Xiao, H. Wu, and Y. Wei. Simple baselines for human pose estimation and tracking. In ECCV, 2018.
[6]
Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In CVPR, 2019.
[7]
Jing Xu, Rui Zhao, Feng Zhu, Huaming Wang, and Wanli Ouyang. Attention Aware Compositional Network for Person Re-Identification. In CVPR, 2018.
[8]
Binghui Chen, Weihong Deng, and Jiani Hu. Mixed High-Order Attention Network for Person Re-Identifification. In ICCV, 2019.
[9]
Vaswani A, Shazeer N, Parmar N, et al. Attention Is All You Need[j]. arXiv, 2017.
[10]
Hermans, A., Beyer, L., and Leibe, B. In defense of the triplet loss for person reidentification. arXiv preprint arXiv: 1703.07737, 2017.
[11]
L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian. Scalable person reidentification: A benchmark. In ICCV, 2015.
[12]
E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi. Performance measures and a data set for multi-target, multi-camera tracking. In ECCV, 2016.
[13]
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. Aggregated Residual Transformations for Deep Neural Networks. In ECCV, 2017.
[14]
Yifan Sun, Liang Zheng, Weijian Deng, and Shengjin Wang. Svdnet for pedestrian retrieval. In ICCV, 2017.
[15]
Chang, X.; Hospedales, T. M.; and Xiang, T. Multi-level factorisation net for person re-identification. In CVPR, 2018.
[16]
Yan Wang, Lequn Wang, Yurong You, and Xu Zou. Resource aware person reidentification across multiple resolutions. In CVPR, 2018.
[17]
Li, W., Zhu, X., and Gong, S. Harmonious attention network for person reidentification. In CVPR, 2018.
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- York University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 15 February 2021
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- Short-paper
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- Refereed limited
Funding Sources
- Natural Science Foundation of China
Conference
CCEAI 2021
CCEAI 2021: 5th International Conference on Control Engineering and Artificial Intelligence
January 14 - 16, 2021
Sanya, China
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