# In Defense of the Classification Loss for Person Re-Identification

arXiv: https://arxiv.org/abs/1809.05864 · HTML: https://mlsanity.com/paper/1809.05864/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2018.

## Built on by (most cited first, 17 of 17)

- [When Large Vision-Language Models Meet Person Re-Identification](https://mlsanity.com/paper/2411.18111/) (2024)
- [Sampling Agnostic Feature Representation for Long-Term Person Re-identification](https://mlsanity.com/paper/2209.09574/) (2022)
- [Rethinking Person Re-identification from a Projection-on-Prototypes Perspective](https://mlsanity.com/paper/2308.10717/) (2023)
- [Enhancing person re-identification via Uncertainty Feature Fusion Method and Auto-weighted Measure Combination](https://mlsanity.com/paper/2405.01101/) (2024)
- [Label Aware Speech Representation Learning For Language Identification](https://mlsanity.com/paper/2306.04374/) (2023)
- [Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification](https://mlsanity.com/paper/2310.01129/) (2023)
- [Resolution based Feature Distillation for Cross Resolution Person Re-Identification](https://mlsanity.com/paper/2109.07871/) (2021)
- [If At First You Don't Succeed: Test Time Re-ranking for Zero-shot, Cross-domain Retrieval](https://mlsanity.com/paper/2303.17703/) (2023)
- [Going Deeper into Semi-supervised Person Re-identification](https://mlsanity.com/paper/2107.11566/) (2021)
- [AdaTriplet-RA: Domain Matching via Adaptive Triplet and Reinforced Attention for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2211.08894/) (2022)
- [Person detection and re-identification in open-world settings of retail stores and public spaces](https://mlsanity.com/paper/2505.00772/) (2025)
- [Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence](https://mlsanity.com/paper/2507.01504/) (2025)
- [Copy and Paste method based on Pose for Re-identification](https://mlsanity.com/paper/2107.10479/) (2021)
- [MASR: Multi-label Aware Speech Representation](https://mlsanity.com/paper/2307.10982/) (2023)
- [On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification](https://mlsanity.com/paper/2403.12606/) (2024)
- [A review of Recent Techniques for Person Re-Identification](https://mlsanity.com/paper/2509.22690/) (2025)
- [Generalization Limits in Vehicle Re-Identification](https://mlsanity.com/paper/2606.01981/) (2026)

## Abstract (arXiv)

The recent research for person re-identification has been focused on two trends. One is learning the part-based local features to form more informative feature descriptors. The other is designing effective metric learning loss functions such as the triplet loss family. We argue that learning global features with classification loss could achieve the same goal, even with some simple and cost-effective architecture design. In this paper, we first explain why the person re-id framework with standard classification loss usually has inferior performance compared to metric learning. Based on that, we further propose a person re-id framework featured by channel grouping and multi-branch strategy, which divides global features into multiple channel groups and learns the discriminative channel group features by multi-branch classification layers. The extensive experiments show that our framework outperforms prior state-of-the-arts in terms of both accuracy and inference speed.