# Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification

arXiv: https://arxiv.org/abs/2109.10498 · HTML: https://mlsanity.com/paper/2109.10498/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 64 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [Re-ranking Person Re-identification with k-reciprocal Encoding](https://mlsanity.com/paper/1701.08398/) (2017)
- [Harmonious Attention Network for Person Re-Identification](https://mlsanity.com/paper/1802.08122/) (2018)
- [Joint Detection and Identification Feature Learning for Person Search](https://mlsanity.com/paper/1604.01850/) (2016)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification](https://mlsanity.com/paper/1711.07027/) (2017)
- [Learning from Synthetic Data for Crowd Counting in the Wild](https://mlsanity.com/paper/1903.03303/) (2019)
- [Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach](https://mlsanity.com/paper/1812.05040/) (2018)
- [Dissecting Person Re-identification from the Viewpoint of Viewpoint](https://mlsanity.com/paper/1812.02162/) (2018)
- [MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?](https://mlsanity.com/paper/2108.09518/) (2021)
- [Domain Adaptation through Synthesis for Unsupervised Person Re-identification](https://mlsanity.com/paper/1804.10094/) (2018)
- [Learning To Simulate](https://mlsanity.com/paper/1810.02513/) (2018)
- [Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification](https://mlsanity.com/paper/2006.12774/) (2020)
- [Simulating Content Consistent Vehicle Datasets with Attribute Descent](https://mlsanity.com/paper/1912.08855/) (2019)
- [UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification](https://mlsanity.com/paper/2012.04268/) (2020)

## Built on by (most cited first, 20 of 20)

- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark](https://mlsanity.com/paper/2306.02898/) (2023)
- [Rethinking Person Re-Identification via Semantic-Based Pretraining](https://mlsanity.com/paper/2110.05074/) (2021)
- [Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval](https://mlsanity.com/paper/2303.15671/) (2023)
- [AutoKary2022: A Large-Scale Densely Annotated Dataset for Chromosome Instance Segmentation](https://mlsanity.com/paper/2303.15839/) (2023)
- [From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identification](https://mlsanity.com/paper/2408.00096/) (2024)
- [Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification](https://mlsanity.com/paper/2304.09498/) (2023)
- [Learning Multi-axis Representation in Frequency Domain for Medical Image Segmentation](https://mlsanity.com/paper/2312.17030/) (2023)
- [Deep Multimodal Fusion for Generalizable Person Re-identification](https://mlsanity.com/paper/2211.00933/) (2022)
- [SubFace: Learning with Softmax Approximation for Face Recognition](https://mlsanity.com/paper/2208.11483/) (2022)
- [Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalization](https://mlsanity.com/paper/2308.03580/) (2023)
- [Supervised Contrastive Learning for Fine-grained Chromosome Recognition](https://mlsanity.com/paper/2312.07623/) (2023)
- [An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval](https://mlsanity.com/paper/2503.22171/) (2025)
- [Learning Discriminative Visual-Text Representation for Polyp Re-Identification](https://mlsanity.com/paper/2307.10625/) (2023)
- [Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning](https://mlsanity.com/paper/2312.02535/) (2023)
- [Beyond Oversmoothing: Evaluating DDPM and MSE for Scalable Speech Synthesis in ASR](https://mlsanity.com/paper/2410.12279/) (2024)
- [A Dual-stage Prompt-driven Privacy-preserving Paradigm for Person Re-Identification](https://mlsanity.com/paper/2511.05092/) (2025)
- [Multi-Grained Vision-Language Alignment for Domain Generalized Person Re-Identification](https://mlsanity.com/paper/2603.14012/) (2026)
- [Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition](https://mlsanity.com/paper/2604.07884/) (2026)
- [3D-VITAL: Visibility-aware Identity Training from Artificial Liftings of 3D Reconstruction model for Cross-View Object Re-Identification](https://mlsanity.com/paper/2610.16692/) (2026)

## Abstract (arXiv)

Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, has attracted great attention from the public eyes. However, existing datasets are limited in quantity, diversity and realisticity, and cannot be efficiently used for re-ID problem. To address this challenge, we manually construct a large-scale person dataset named FineGPR with fine-grained attribute annotations. Moreover, aiming to fully exploit the potential of FineGPR and promote the efficient training from millions of synthetic data, we propose an attribute analysis pipeline called AOST, which dynamically learns attribute distribution in real domain, then eliminates the gap between synthetic and real-world data and thus is freely deployed to new scenarios. Experiments conducted on benchmarks demonstrate that FineGPR with AOST outperforms (or is on par with) existing real and synthetic datasets, which suggests its feasibility for re-ID task and proves the proverbial less-is-more principle. Our synthetic FineGPR dataset is publicly available at https://github.com/JeremyXSC/FineGPR.