Fully Unsupervised Person Re-Identification by Enhancing Cluster Samples | |
Chen, Xiumei2,3; Zheng, Xiangtao3; Zhu, Kaijian1; Lu, Xiaoqiang3 | |
2021-12-16 | |
会议日期 | 2021-12-16 |
会议地点 | Virtual, Online, China |
关键词 | unsupervised person re-identification sample transformation convolutional neural network feature extraction |
DOI | 10.1145/3507971.3507984 |
页码 | 70-73 |
英文摘要 | Fully unsupervised person re-identification aims to train a discriminative model with unlabeled person images. Most existing methods first generate pseudo labels by clustering image features (convolutional features) and then fine-tune the convolutional neural network (CNN) with pseudo labels. However, these methods are greatly limited by the quality of the pseudo labels. In this paper, a cluster sample enhancement method is introduced to increase the reliability of pseudo-label samples to facilitate the CNN training. Specifically, when generating pseudo labels, only the samples with high-confidence pseudo-label predictions are selected. In addition, to enhance the selected samples for training, two different image transformations are adopted and coupled with specific-design loss functions to boost the model performance. Experiments demonstrate the effectiveness of the proposed method. Concretely, the proposed method achieves 87.1% rank-1 and 70.2% mAP accuracy on Market-1501. © 2021 ACM. |
产权排序 | 1 |
会议录 | 2021 7th International Conference on Communication and Information Processing, ICCIP 2021 |
会议录出版者 | Association for Computing Machinery |
语种 | 英语 |
ISBN号 | 9781450385190 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/95804] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
作者单位 | 1.State Key Laboratory of Astronautic Dynamics, Xi'an Satellite Control Center, Xi'an, Shaanxi, China 2.University of Chinese Academy of Sciences Beijing, China; 3.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi, China; |
推荐引用方式 GB/T 7714 | Chen, Xiumei,Zheng, Xiangtao,Zhu, Kaijian,et al. Fully Unsupervised Person Re-Identification by Enhancing Cluster Samples[C]. 见:. Virtual, Online, China. 2021-12-16. |
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