# A Culturally-Aware Benchmark for Person Re-Identification in Modest Attire

arXiv: https://arxiv.org/abs/2412.18874 · HTML: https://mlsanity.com/paper/2412.18874/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Builds on (most cited first, 17 of 17)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels](https://mlsanity.com/paper/2211.13977/) (2022)
- [Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks](https://mlsanity.com/paper/2303.17602/) (2023)
- [Unsupervised Pre-training for Person Re-identification](https://mlsanity.com/paper/2012.03753/) (2020)
- [Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark](https://mlsanity.com/paper/2306.02898/) (2023)
- [Large-Scale Spatio-Temporal Person Re-identification: Algorithms and Benchmark](https://mlsanity.com/paper/2105.15076/) (2021)
- [Large-Scale Pre-training for Person Re-identification with Noisy Labels](https://mlsanity.com/paper/2203.16533/) (2022)
- [Region-based Quality Estimation Network for Large-scale Person Re-identification](https://mlsanity.com/paper/1711.08766/) (2017)
- [SoccerNet 2022 Challenges Results](https://mlsanity.com/paper/2210.02365/) (2022)
- [Ten Years of Pedestrian Detection, What Have We Learned?](https://mlsanity.com/paper/1411.4304/) (2014)

## Built on by (most cited first, 6 of 6)

- [Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification](https://mlsanity.com/paper/2508.06831/) (2025)
- [Illuminating Visual Identity in Universal Multimodal Embeddings](https://mlsanity.com/paper/2608.01794/) (2026)
- [Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States](https://mlsanity.com/paper/2509.11247/) (2025)
- [What really matters for person re-identification? A Mixture-of-Experts Framework for Semantic Attribute Importance](https://mlsanity.com/paper/2512.08697/) (2025)
- [Person Re-ID in 2025: Supervised, Self-Supervised, and Language-Aligned. What Works?](https://mlsanity.com/paper/2601.20598/) (2026)
- [Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification](https://mlsanity.com/paper/2606.02242/) (2026)

## Abstract (arXiv)

Person Re-Identification (ReID) is a fundamental task in computer vision with critical applications in surveillance and security. Despite progress in recent years, most existing ReID models often struggle to generalize across diverse cultural contexts, particularly in Islamic regions like Iran, where modest clothing styles are prevalent. Existing datasets predominantly feature Western and East Asian fashion, limiting their applicability in these settings. To address this gap, we introduce Iran University of Science and Technology Person Re-Identification (IUST_PersonReId), a dataset designed to reflect the unique challenges of ReID in new cultural environments, emphasizing modest attire and diverse scenarios from Iran, including markets, campuses, and mosques. Experiments on IUST_PersonReId with state-of-the-art models, such as Semantic Controllable Self-supervised Learning (SOLIDER) and Contrastive Language-Image Pretraining Re-Identification (CLIP-ReID), reveal significant performance drops compared to benchmarks like Market1501 and Multi-Scene MultiTime (MSMT17), specifically, SOLIDER shows a drop of 50.75% and 23.01% Mean Average Precision (mAP) compared to Market1501 and MSMT17 respectively, while CLIP-ReID exhibits a drop of 38.09% and 21.74% mAP, highlighting the challenges posed by occlusion and limited distinctive features. Sequence-based evaluations show improvements by leveraging temporal context, emphasizing the dataset's potential for advancing culturally sensitive and robust ReID systems. IUST_PersonReId offers a critical resource for addressing fairness and bias in ReID research globally.