# Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification

arXiv: https://arxiv.org/abs/1710.00478 · HTML: https://mlsanity.com/paper/1710.00478/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2017.

## Built on by (most cited first, 12 of 12)

- [Person Re-identification Method Based on Color Attack and Joint Defence](https://mlsanity.com/paper/2111.09571/) (2021)
- [DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition](https://mlsanity.com/paper/2303.14953/) (2023)
- [Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking](https://mlsanity.com/paper/2407.13646/) (2024)
- [Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline](https://mlsanity.com/paper/2205.02692/) (2022)
- [Rethinking Person Re-Identification via Semantic-Based Pretraining](https://mlsanity.com/paper/2110.05074/) (2021)
- [Clothes-Changing Person Re-identification Based On Skeleton Dynamics](https://mlsanity.com/paper/2503.10759/) (2025)
- [Global-Local Dynamic Feature Alignment Network for Person Re-Identification](https://mlsanity.com/paper/2109.05759/) (2021)
- [Improving Image co-segmentation via Deep Metric Learning](https://mlsanity.com/paper/2103.10670/) (2021)
- [Hard Example Guided Hashing for Image Retrieval](https://mlsanity.com/paper/2112.13565/) (2021)
- [Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction](https://mlsanity.com/paper/2301.11986/) (2023)
- [Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination](https://mlsanity.com/paper/2310.06339/) (2023)
- [Beyond Seen Bounds: Class-Centric Polarization for Single-Domain Generalized Deep Metric Learning](https://mlsanity.com/paper/2601.09121/) (2026)

## Abstract (arXiv)

Person re-identification (ReID) is an important task in computer vision. Recently, deep learning with a metric learning loss has become a common framework for ReID. In this paper, we also propose a new metric learning loss with hard sample mining called margin smaple mining loss (MSML) which can achieve better accuracy compared with other metric learning losses, such as triplet loss. In experi- ments, our proposed methods outperforms most of the state-of-the-art algorithms on Market1501, MARS, CUHK03 and CUHK-SYSU.