Aimed at the problems that while paying attention to the text-based person re-identification models often relying on global and local feature in alignment,very often the inter--modal and intra-modal correlations are in negative,a cross-modal pedestrian re-identification method is proposed based on rela-tionship mining.The method includes a dual-stream network backbone,negative similarity mining mod-ule,and relationship encoder module.Firstly,the global and the local feature are in alignment through the dual-stream network backbone.Secondly the granularity of feature discrimination is enhanced by using the negative similarity mining module,and the similar incorrect results are filtered out.Finally,the relation-ship encoder module is utilized for respectively learning the latent relationship information in both the im-age and text,achieving relationship-level feature alignment.The experimental results on the CUHK-PEDES dataset and the ICFG-PEDES dataset show that this method achieves recognition accuracy higher.
关键词
行人重识别/多粒度图像/文本对齐/关系特征融合/卷积神经网络/全局特征/局部特征
Key words
person re-identification/multi-granularity image/text alignments/relationship feature fusion/convolutional neural network/global feature/local feature
维普
万方数据