Clustering-Guided Pairwise Metric Triplet Loss for Person Reidentification

Most of the loss functions proposed for person reidentification (Re-ID) are expected to be easy to deploy, efficiently improve network performance, and will not introduce redundant parameters. This study proposes a no-parameter and generic clustering-guided pairwise metric triplet (CPM-Triplet) loss based on the hard sample mining triplet loss for the metric learning loss. CPM-Triplet loss deploys two metrics: 1) the Euclidean metric and 2) the cosine metric, to complementarily improve the metric learning of the model. Paralleled to the Euclidean metric, the cosine metric quantifies the sample similarity in a different way to the Euclidean metric, which takes a different perspective to explore the distribution of samples. But the pairwise metric mainly improves the precision between dissimilar samples of the same label and could not solve the problem of excessive outliers. Therefore, the clustering-guided correction term was deployed to apply to all samples with the same label to mine the similarity in the samples, while weakening the influence of outliers in CPM-Triplet loss. Experiments conducted on four benchmark data sets show that the combination of the CPM-Triplet loss and the widely used Bag-of-Tricks baseline generally outperforms the baseline and numerous state-of-the-art methods studied in this article. The source code would be available at https://github.com/weiyu-zeng/CPM-Triplet-loss .