Multi-Stream Refining Network for Person Re-Identification
Multi-Stream Refining Network for Person Re-Identification
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DOI：
10.1109/access.2020.3048119
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Xu Wang;Y. Huang;Qicong Wang;Yan Chen;Yehu Shen
中科院分区：
文献类型：
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作者：
Xu Wang;Y. Huang;Qicong Wang;Yan Chen;Yehu Shen
视点变化、位姿变化和背景杂波对人再识别相似性评价有不利影响。由于其显著性和可靠性，人物显著性已被应用于模特外貌特征。然而，现有的深度方法并没有充分利用这些有价值的信息来计算人物图像的相似度。为此，我们提出了一种新的基于多流精炼的深度多任务学习方案，该方案通过聚合网络中的多阶段显著嵌入特征来提高检索性能。具体而言，将主干网划分为4个阶段，并引入信道重要性自学习子模块自适应增强显著信道的重要性。同时，利用增强子模块从信道中提取共同信息和不同信息。最后，采用四阶段分支相结合的多流多任务学习框架学习判别特征。与最先进的方法相比，我们的模型在三个公开可用的数据集（即Market-1501， MSMT17和CUHK03）上取得了具有竞争力的表现。实验结果表明，本文方法在Rank-1/mAP上的准确率分别达到95.67%/88.51%、87.53%/65.54%和89.32%/78.99%。
Viewpoint change, pose variation and background clutter have adverse impacts on similarity evaluation for person re-identification. Because of its distinction and reliability, person saliency has been applied to model person appearance characteristics. However, such valuable information is not fully exploited to compute similarities of person images with existing deep methods. To this end, we present a novel multi-stream refining based deep multi-task learning scheme that aggregates multi-stage salient embedding features in the network to boost the retrieval performance. Specifically, the backbone network is divided into four stages and a channel significance self-learning sub-module is introduced to strengthen the importance of saliency channels adaptively. Meanwhile, an enhancement sub-module is employed to extract the common information and different information from the channels. Finally, a multi-stream multi-task learning framework combining four-stage branches is adopted to learn discriminative features. Compared with the state-of-the-art approaches, our model achieves competitive performance on three publicly available datasets, i.e., Market-1501, MSMT17, and CUHK03. The experimental results demonstrate the superiority of our method, which achieves 95.67%/88.51%, 87.53%/65.54%, and 89.32%/78.99% on Rank-1/mAP, respectively.