Biclustering Collaborative Learning for Cross-Domain Person Re-Identification

Abstract

In the cross-domain person re-identification (ReID) method based on clustering, the performance of the model depends heavily on the quality of the information it obtains from clustering. To improve the reliability of the clustering information obtained by the model, we propose a biclustering collaborative learning (BCL) framework derived from an identity disentanglement adaptation network (IDA-Net). IDA-Net encodes the identity and style of the input image and transfers the style on the premise of maintaining identity consistency. By comparing the clustering results obtained on the same dataset before and after the transfer process, BCL can select hard samples with higher confidence for model optimization. In each iteration, we design a conditional batch hard triplet loss to optimize the two networks. Extensive experiments on large-scale datasets (Maket1501, DukeMTMC-reID and MSMT17) demonstrate the superior performance of BCL over the state-of-the-art methods.