Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification | |
Ke, Han1,2![]() ![]() ![]() | |
2020-08 | |
会议名称 | European Conference on Computer Vision |
卷号 | 12371 |
会议日期 | 2020.8-23-2020.8.28 |
会议地点 | 线上 |
摘要 | Low-resolution person re-identification (LR re-id) is a challenging task with low-resolution probes and high-resolution gallery images. To address the resolution mismatch, existing methods typically recover missing details for low-resolution probes by super-resolution. However, they usually pre-specify fixed scale factors for all images, and ignore the fact that choosing a preferable scale factor for certain image content probably greatly benefits the identification. In this paper, we propose a novel Prediction, Recovery and Identification (PRI) model for LR re-id, which adaptively recovers missing details by predicting a preferable scale factor based on the image content. To deal with the lack of ground-truth optimal scale factors, our model contains a self-supervised scale factor metric that automatically generates dynamic soft labels. The generated labels indicate probabilities that each scale factor is optimal, which are used as guidance to enhance the content-aware scale factor prediction. Consequently, our model can more accurately predict and recover the content-aware details, and achieve state-of-the-art performances on four LR re-id datasets. |
收录类别 | EI |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52191 |
专题 | 模式识别实验室 |
通讯作者 | Yan, Huang |
作者单位 | 1.Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) 2.School of Future Technology, University of Chinese Academy of Sciences (UCAS) 3.Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) 4.Chinese Academy of Sciences, Artificial Intelligence Research (CAS-AIR) |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Ke, Han,Yan, Huang,Zerui, Chen,et al. Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification[C],2020. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ECCV 2020.pdf(677KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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