Transferring discriminative knowledge via connective momentum clustering on person re-identification
作者：
Highlights：
• Unsupervised domain adaptation (UDA) in person re-identification (ReID).
• Connective Momentum Clustering (CMC) framework to build a connection estimator via graph convolutional networks (GCN).
• Normalize the data stream separately to decouple different distribution.
• Extensive cross-database experiments on Duke and Market databases.
• SOTA performance on ReID domain adaption.
摘要
•Unsupervised domain adaptation (UDA) in person re-identification (ReID).•Connective Momentum Clustering (CMC) framework to build a connection estimator via graph convolutional networks (GCN).•Normalize the data stream separately to decouple different distribution.•Extensive cross-database experiments on Duke and Market databases.•SOTA performance on ReID domain adaption.
论文关键词：Person re-identification,Unsupervised domain adaptation,Graph convolutional networks,Momentum mechanism,Batch normalization
论文评审过程：Received 27 April 2021, Revised 31 December 2021, Accepted 31 January 2022, Available online 5 February 2022, Version of Record 16 February 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108569
