# Person Re-Identification with a Locally Aware Transformer

arXiv: https://arxiv.org/abs/2106.03720 · HTML: https://mlsanity.com/paper/2106.03720/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 35 references that are in the map.
- First posted on arXiv 7 Jun 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Visual Transformers: Token-based Image Representation and Processing for Computer Vision](https://mlsanity.com/paper/2006.03677/) (2020)
- [DeepViT: Towards Deeper Vision Transformer](https://mlsanity.com/paper/2103.11886/) (2021)
- [ABD-Net: Attentive but Diverse Person Re-Identification](https://mlsanity.com/paper/1908.01114/) (2019)
- [Training Vision Transformers for Image Retrieval](https://mlsanity.com/paper/2102.05644/) (2021)
- [Toward Transformer-Based Object Detection](https://mlsanity.com/paper/2012.09958/) (2020)
- [A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking](https://mlsanity.com/paper/1711.10378/) (2017)
- [AlignedReID: Surpassing Human-Level Performance in Person Re-Identification](https://mlsanity.com/paper/1711.08184/) (2017)
- [Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification](https://mlsanity.com/paper/1910.10111/) (2019)
- [Universal Language Model Fine-tuning for Text Classification](https://mlsanity.com/paper/1801.06146/) (2018)
- [Spatial-Temporal Person Re-identification](https://mlsanity.com/paper/1812.03282/) (2018)
- [Deep Representation Learning with Part Loss for Person Re-Identification](https://mlsanity.com/paper/1707.00798/) (2017)
- [Multi-task Learning with Coarse Priors for Robust Part-aware Person Re-identification](https://mlsanity.com/paper/2003.08069/) (2020)
- [Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification](https://mlsanity.com/paper/1912.01300/) (2019)
- [Lightweight Multi-Branch Network for Person Re-Identification](https://mlsanity.com/paper/2101.10774/) (2021)
- [Parameter-Free Spatial Attention Network for Person Re-Identification](https://mlsanity.com/paper/1811.12150/) (2018)
- [Learning Diverse Features with Part-Level Resolution for Person Re-Identification](https://mlsanity.com/paper/2001.07442/) (2020)

## Built on by (most cited first, 19 of 19)

- [Recent Advances in Vision Transformer: A Survey and Outlook of Recent Work](https://mlsanity.com/paper/2203.01536/) (2022)
- [Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification](https://mlsanity.com/paper/2310.17218/) (2023)
- [Rotation Invariant Transformer for Recognizing Object in UAVs](https://mlsanity.com/paper/2311.02559/) (2023)
- [GraFT: Gradual Fusion Transformer for Multimodal Re-Identification](https://mlsanity.com/paper/2310.16856/) (2023)
- [Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2211.12280/) (2022)
- [Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2307.03240/) (2023)
- [Product Re-identification System in Fully Automated Defect Detection](https://mlsanity.com/paper/2112.10324/) (2021)
- [Motion-Aware Transformer For Occluded Person Re-identification](https://mlsanity.com/paper/2202.04243/) (2022)
- [Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification](https://mlsanity.com/paper/2304.07883/) (2023)
- [3D Semantic MapNet: Building Maps for Multi-Object Re-Identification in 3D](https://mlsanity.com/paper/2403.13190/) (2024)
- [Not All Starting Points Are Equal: Pre-trained Priors and Their Outsized Impact on Person Identification](https://mlsanity.com/paper/2507.17640/) (2025)
- [Person Re-Identification](https://mlsanity.com/paper/2204.13158/) (2022)
- [Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal Data](https://mlsanity.com/paper/2305.00320/) (2023)
- [PAFormer: Part Aware Transformer for Person Re-identification](https://mlsanity.com/paper/2408.05918/) (2024)
- [Revisiting the Role of Texture in 3D Person Re-identification](https://mlsanity.com/paper/2410.00348/) (2024)
- [Cross-Modal Synergies: Unveiling the Potential of Motion-Aware Fusion Networks in Handling Dynamic and Static ReID Scenarios](https://mlsanity.com/paper/2502.00665/) (2025)
- [CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble Fusion](https://mlsanity.com/paper/2508.04036/) (2025)
- [A review of Recent Techniques for Person Re-Identification](https://mlsanity.com/paper/2509.22690/) (2025)
- [Multi-Grained Vision-Language Alignment for Domain Generalized Person Re-Identification](https://mlsanity.com/paper/2603.14012/) (2026)

## Abstract (arXiv)

Person Re-Identification is an important problem in computer vision-based surveillance applications, in which the same person is attempted to be identified from surveillance photographs in a variety of nearby zones. At present, the majority of Person re-ID techniques are based on Convolutional Neural Networks (CNNs), but Vision Transformers are beginning to displace pure CNNs for a variety of object recognition tasks. The primary output of a vision transformer is a global classification token, but vision transformers also yield local tokens which contain additional information about local regions of the image. Techniques to make use of these local tokens to improve classification accuracy are an active area of research. We propose a novel Locally Aware Transformer (LA-Transformer) that employs a Parts-based Convolution Baseline (PCB)-inspired strategy for aggregating globally enhanced local classification tokens into an ensemble of √N classifiers, where N is the number of patches. An additional novelty is that we incorporate blockwise fine-tuning which further improves re-ID accuracy. LA-Transformer with blockwise fine-tuning achieves rank-1 accuracy of 98.27 % with standard deviation of 0.13 on the Market-1501 and 98.7% with standard deviation of 0.2 on the CUHK03 dataset respectively, outperforming all other state-of-the-art published methods at the time of writing.