# Cluster Loss for Person Re-Identification

## TL;DR

This paper introduces a cluster loss function for person re-identification that enhances clustering performance by increasing inter-class variation and reducing intra-class variation, outperforming triplet loss.

## Contribution

The paper proposes a novel cluster loss and a batch hard training mechanism to improve person ReID accuracy and generalization in clustering tasks.

## Key findings

- Cluster loss yields larger inter-class and smaller intra-class variations.
- The method achieves higher accuracy on test sets compared to triplet loss.
- Batch hard training accelerates convergence and improves results.

## Abstract

Person re-identification (ReID) is an important problem in computer vision, especially for video surveillance applications. The problem focuses on identifying people across different cameras or across different frames of the same camera. The main challenge lies in identifying the similarity of the same person against large appearance and structure variations, while differentiating between individuals. Recently, deep learning networks with triplet loss have become a common framework for person ReID. However, triplet loss focuses on obtaining correct orders on the training set. We demonstrate that it performs inferior in a clustering task. In this paper, we design a cluster loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve higher accuracy on the test set especially for a clustering task. We also introduce a batch hard training mechanism for improving the results and faster convergence of training.