Domain-adaptive Person Re-identification without Cross-camera Paired Samples

判别式 人工智能 计算机科学 匹配(统计) 特征(语言学) 领域(数学分析) 身份(音乐) 一般化 模式识别(心理学) 鉴定(生物学) 任务(项目管理) 计算机视觉 数学 工程类 统计 数学分析 哲学 物理 生物 植物 系统工程 语言学 声学
作者
Huafeng Li,Yanmei Mao,Yafei Zhang,Guanqiu Qi,Zhengtao Yu
出处
期刊:Cornell University - arXiv 被引量:1
标识
DOI:10.48550/arxiv.2307.06533
摘要

Existing person re-identification (re-ID) research mainly focuses on pedestrian identity matching across cameras in adjacent areas. However, in reality, it is inevitable to face the problem of pedestrian identity matching across long-distance scenes. The cross-camera pedestrian samples collected from long-distance scenes often have no positive samples. It is extremely challenging to use cross-camera negative samples to achieve cross-region pedestrian identity matching. Therefore, a novel domain-adaptive person re-ID method that focuses on cross-camera consistent discriminative feature learning under the supervision of unpaired samples is proposed. This method mainly includes category synergy co-promotion module (CSCM) and cross-camera consistent feature learning module (CCFLM). In CSCM, a task-specific feature recombination (FRT) mechanism is proposed. This mechanism first groups features according to their contributions to specific tasks. Then an interactive promotion learning (IPL) scheme between feature groups is developed and embedded in this mechanism to enhance feature discriminability. Since the control parameters of the specific task model are reduced after division by task, the generalization ability of the model is improved. In CCFLM, instance-level feature distribution alignment and cross-camera identity consistent learning methods are constructed. Therefore, the supervised model training is achieved under the style supervision of the target domain by exchanging styles between source-domain samples and target-domain samples, and the challenges caused by the lack of cross-camera paired samples are solved by utilizing cross-camera similar samples. In experiments, three challenging datasets are used as target domains, and the effectiveness of the proposed method is demonstrated through four experimental settings.
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