Attentive part-aware networks for partial person re-identification | |
Lijuan Huo1; Chunfeng Song2,3,4; Zhengyi Liu1; Zhaoxiang Zhang2,3 | |
2020 | |
会议名称 | International Conference on Pattern Recognition (ICPR) |
会议日期 | 2021.06.10 |
会议地点 | Milan, Italy |
摘要 | Partial person re-identification (re-ID) refers to re-identify a person through occluded images. It suffers from two major challenges, i.e., insufficient training data and incomplete probe image. In this paper, we introduce a part-aware learning method for partial person re-identification. On the one hand, we adopt data augmentation operation to enrich the training data and improve the robustness of the model. On the other hand, we intuitively find that the partial person images usually have fixed percentages of parts, therefore, in partial person re-ID task, the probe image could be cropped from the pictures and divided into several different partial types following fixed ratios. Based on the cropped images, we propose the Cropping Type Consistency (CTC) loss to classify the cropping types of partial images. Moreover, in order to help the network better fit the generated and cropped data, we incorporate the Block Attention Mechanism (BAM) into the framework for attentive learning. To enhance the retrieval performance in the inference stage, we implement cropping on gallery images according to the predicted types of probe partial images. Through calculating feature distances between the partial image and the cropped holistic gallery images, the model can recognize the right person from the gallery. To validate the effectiveness of our approach, we conduct extensive experiments on the partial re- ID benchmarks and achieve state-of-the-art performance. |
是否为代表性论文 | 否 |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 多模态协同认知 |
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/173211/51618 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Chunfeng Song |
作者单位 | 1.Anhui University 2.Institute of Automation, Chinese Academy of Sciences (CASIA) 3.UCAS 4.Artificial Intelligence ResearchChinese Academy of Sciences |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Lijuan Huo,Chunfeng Song,Zhengyi Liu,et al. Attentive part-aware networks for partial 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 | ||
Attentive_Part-aware(2109KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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