  Part feature learning is critical for fine-grained semantic understanding in
vehicle re-identification. However, existing approaches directly model part
features and global features, which can easily lead to serious gradient
vanishing issues due to their unequal feature information and unreliable
pseudo-labels for unsupervised vehicle re-identification. To address this
problem, in this paper, we propose a simple Triplet Contrastive Representation
Learning (TCRL) framework which leverages cluster features to bridge the part
features and global features for unsupervised vehicle re-identification.
Specifically, TCRL devises three memory banks to store the instance/cluster
features and proposes a Proxy Contrastive Loss (PCL) to make contrastive
learning between adjacent memory banks, thus presenting the associations
between the part and global features as a transition of the part-cluster and
cluster-global associations. Since the cluster memory bank copes with all the
vehicle features, it can summarize them into a discriminative feature
representation. To deeply exploit the instance/cluster information, TCRL
proposes two additional loss functions. For the instance-level feature, a
Hybrid Contrastive Loss (HCL) re-defines the sample correlations by approaching
the positive instance features and pushing the all negative instance features
away. For the cluster-level feature, a Weighted Regularization Cluster
Contrastive Loss (WRCCL) refines the pseudo labels by penalizing the mislabeled
images according to the instance similarity. Extensive experiments show that
TCRL outperforms many state-of-the-art unsupervised vehicle re-identification
approaches.
