# HAT: Hierarchical Aggregation Transformers for Person Re-identification

arXiv: https://arxiv.org/abs/2107.05946 · HTML: https://mlsanity.com/paper/2107.05946/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 63 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 30 of 48)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [Rethinking Spatial Dimensions of Vision Transformers](https://mlsanity.com/paper/2103.16302/) (2021)
- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [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)
- [Joint Discriminative and Generative Learning for Person Re-identification](https://mlsanity.com/paper/1904.07223/) (2019)
- [Style Normalization and Restitution for Generalizable Person Re-identification](https://mlsanity.com/paper/2005.11037/) (2020)

## Built on by (most cited first, 30 of 39)

- [Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2212.00226/) (2022)
- [Self-Supervised Pre-Training for Transformer-Based Person Re-Identification](https://mlsanity.com/paper/2111.12084/) (2021)
- [Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2403.10254/) (2024)
- [TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation](https://mlsanity.com/paper/2312.09612/) (2023)
- [Cascade Transformers for End-to-End Person Search](https://mlsanity.com/paper/2203.09642/) (2022)
- [DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2412.10650/) (2024)
- [IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2503.10324/) (2025)
- [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024)
- [Recent Advances in Vision Transformer: A Survey and Outlook of Recent Work](https://mlsanity.com/paper/2203.01536/) (2022)
- [Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2304.14122/) (2023)
- [MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt](https://mlsanity.com/paper/2412.10707/) (2024)
- [Fully Transformer Network for Change Detection of Remote Sensing Images](https://mlsanity.com/paper/2210.00757/) (2022)
- [TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images](https://mlsanity.com/paper/2310.14214/) (2023)
- [Rotation Invariant Transformer for Recognizing Object in UAVs](https://mlsanity.com/paper/2311.02559/) (2023)
- [Adaptive High-Frequency Transformer for Diverse Wildlife Re-Identification](https://mlsanity.com/paper/2410.06977/) (2024)
- [Other Tokens Matter: Exploring Global and Local Features of Vision Transformers for Object Re-Identification](https://mlsanity.com/paper/2404.14985/) (2024)
- [Unity is Strength: Unifying Convolutional and Transformeral Features for Better Person Re-Identification](https://mlsanity.com/paper/2412.17239/) (2024)
- [LATex: Leveraging Attribute-based Text Knowledge for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2503.23722/) (2025)
- [Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2211.12280/) (2022)
- [Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification](https://mlsanity.com/paper/2303.10976/) (2023)
- [Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark](https://mlsanity.com/paper/2308.16486/) (2023)
- [Signal: Selective Interaction and Global-local Alignment for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2511.17965/) (2025)
- [CLIP-SCGI: Synthesized Caption-Guided Inversion for Person Re-Identification](https://mlsanity.com/paper/2410.09382/) (2024)
- [What Makes You Unique? Attribute Prompt Composition for Object Re-Identification](https://mlsanity.com/paper/2509.18715/) (2025)
- [Part Representation Learning with Teacher-Student Decoder for Occluded Person Re-identification](https://mlsanity.com/paper/2312.09797/) (2023)
- [Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification](https://mlsanity.com/paper/2410.15613/) (2024)
- [STMI: Segmentation-Guided Token Modulation with Cross-Modal Hypergraph Interaction for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2603.00695/) (2026)
- [Prompting Continual Person Search](https://mlsanity.com/paper/2410.19239/) (2024)
- [Context Sensing Attention Network for Video-based Person Re-identification](https://mlsanity.com/paper/2207.02631/) (2022)
- [Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification](https://mlsanity.com/paper/2304.07883/) (2023)

## Abstract (arXiv)

Recently, with the advance of deep Convolutional Neural Networks (CNNs), person Re-Identification (Re-ID) has witnessed great success in various applications. However, with limited receptive fields of CNNs, it is still challenging to extract discriminative representations in a global view for persons under non-overlapped cameras. Meanwhile, Transformers demonstrate strong abilities of modeling long-range dependencies for spatial and sequential data. In this work, we take advantages of both CNNs and Transformers, and propose a novel learning framework named Hierarchical Aggregation Transformer (HAT) for image-based person Re-ID with high performance. To achieve this goal, we first propose a Deeply Supervised Aggregation (DSA) to recurrently aggregate hierarchical features from CNN backbones. With multi-granularity supervisions, the DSA can enhance multi-scale features for person retrieval, which is very different from previous methods. Then, we introduce a Transformer-based Feature Calibration (TFC) to integrate low-level detail information as the global prior for high-level semantic information. The proposed TFC is inserted to each level of hierarchical features, resulting in great performance improvements. To our best knowledge, this work is the first to take advantages of both CNNs and Transformers for image-based person Re-ID. Comprehensive experiments on four large-scale Re-ID benchmarks demonstrate that our method shows better results than several state-of-the-art methods. The code is released at https://github.com/AI-Zhpp/HAT.