Multi Loss Function for Cross-Modality Person Re-Identification

Furong Liu, Fengsui Wang, Jingang Chen, Qisheng Wang
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Abstract

For cross-modality person re-identiflcation, the intra-class difference between visible images and infrared images of the same identity is large, and how to reduce this intra-class difference has become the key of cross-modality person re-identification. Therefore, we proposed a multi-loss function for cross-modality person re-identification. Firstly, the global attention mechanism was embedded in the Resnet50 network to retain non-local feature information. Secondly, generalized-mean pooling is used to increase feature information extraction for different fine-grained regions by adjusting parameters. Finally, we design a new total loss function to supervise network learning and improve model accuracy. The proposed method achieves an average accuracy of 54.18% and 78.40% in the SYSU-MM01 and RegDB datasets. The experimental results show that the proposed method can effectively improve the accuracy of cross-modality person re-identiflcation.
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跨模态人再识别的多重损失函数
对于跨模态人物再识别,同一身份的可见光图像与红外图像的类内差异较大,如何减小这种类内差异成为跨模态人物再识别的关键。因此,我们提出了一个用于跨模态人再识别的多重损失函数。首先,在Resnet50网络中嵌入全局关注机制,保留非局部特征信息;其次,采用广义均值池化方法,通过调整参数,增加对不同细粒度区域的特征信息提取;最后,我们设计了一个新的总损失函数来监督网络学习,提高模型的准确性。该方法在SYSU-MM01和RegDB数据集上的平均准确率分别为54.18%和78.40%。实验结果表明,该方法能有效提高跨模态人再识别的准确率。
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