# IAN: The Individual Aggregation Network for Person Search

arXiv: https://arxiv.org/abs/1705.05552 · HTML: https://mlsanity.com/paper/1705.05552/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 29 of 29)

- [Anchor-Free Person Search](https://mlsanity.com/paper/2103.11617/) (2021)
- [Sequential End-to-end Network for Efficient Person Search](https://mlsanity.com/paper/2103.10148/) (2021)
- [Cascade Transformers for End-to-End Person Search](https://mlsanity.com/paper/2203.09642/) (2022)
- [PSTR: End-to-End One-Step Person Search With Transformers](https://mlsanity.com/paper/2204.03340/) (2022)
- [Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search](https://mlsanity.com/paper/2102.10795/) (2021)
- [Exploring Visual Context for Weakly Supervised Person Search](https://mlsanity.com/paper/2106.10506/) (2021)
- [Weakly Supervised Person Search with Region Siamese Networks](https://mlsanity.com/paper/2109.06109/) (2021)
- [Ground-to-Aerial Person Search: Benchmark Dataset and Approach](https://mlsanity.com/paper/2308.12712/) (2023)
- [Gallery Filter Network for Person Search](https://mlsanity.com/paper/2210.12903/) (2022)
- [Domain Adaptive Person Search](https://mlsanity.com/paper/2207.11898/) (2022)
- [Self-similarity Driven Scale-invariant Learning for Weakly Supervised Person Search](https://mlsanity.com/paper/2302.12986/) (2023)
- [Person Search Challenges and Solutions: A Survey](https://mlsanity.com/paper/2105.01605/) (2021)
- [Efficient Person Search: An Anchor-Free Approach](https://mlsanity.com/paper/2109.00211/) (2021)
- [Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Search](https://mlsanity.com/paper/2302.04607/) (2023)
- [Making Person Search Enjoy the Merits of Person Re-identification](https://mlsanity.com/paper/2108.10536/) (2021)
- [Learning Context-Aware Embedding for Person Search](https://mlsanity.com/paper/2111.14316/) (2021)
- [PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search](https://mlsanity.com/paper/2210.03433/) (2022)
- [Sequential Transformer for End-to-End Person Search](https://mlsanity.com/paper/2211.04323/) (2022)
- [LEAPS: End-to-End One-Step Person Search With Learnable Proposals](https://mlsanity.com/paper/2303.11859/) (2023)
- [DDAM-PS: Diligent Domain Adaptive Mixer for Person Search](https://mlsanity.com/paper/2310.20706/) (2023)
- [Fast One-Stage Unsupervised Domain Adaptive Person Search](https://mlsanity.com/paper/2405.02832/) (2024)
- [Leveraging Prior Knowledge of Diffusion Model for Person Search](https://mlsanity.com/paper/2510.01841/) (2025)
- [Multi-Attribute Enhancement Network for Person Search](https://mlsanity.com/paper/2102.07968/) (2021)
- [Subtask-dominated Transfer Learning for Long-tail Person Search](https://mlsanity.com/paper/2112.00527/) (2021)
- [End-to-end Person Search Sequentially Trained on Aggregated Dataset](https://mlsanity.com/paper/2201.09604/) (2022)
- [Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos](https://mlsanity.com/paper/2308.04322/) (2023)
- [Generalizable Person Search on Open-world User-Generated Video Content](https://mlsanity.com/paper/2310.10068/) (2023)
- [Prototype-Guided Text-based Person Search based on Rich Chinese Descriptions](https://mlsanity.com/paper/2312.14834/) (2023)
- [The Research of Group Re-identification from Multiple Cameras](https://mlsanity.com/paper/2407.14620/) (2024)

## Abstract (arXiv)

Person search in real-world scenarios is a new challenging computer version task with many meaningful applications. The challenge of this task mainly comes from: (1) unavailable bounding boxes for pedestrians and the model needs to search for the person over the whole gallery images; (2) huge variance of visual appearance of a particular person owing to varying poses, lighting conditions, and occlusions. To address these two critical issues in modern person search applications, we propose a novel Individual Aggregation Network (IAN) that can accurately localize persons by learning to minimize intra-person feature variations. IAN is built upon the state-of-the-art object detection framework, i.e., faster R-CNN, so that high-quality region proposals for pedestrians can be produced in an online manner. In addition, to relieve the negative effect caused by varying visual appearances of the same individual, IAN introduces a novel center loss that can increase the intra-class compactness of feature representations. The engaged center loss encourages persons with the same identity to have similar feature characteristics. Extensive experimental results on two benchmarks, i.e., CUHK-SYSU and PRW, well demonstrate the superiority of the proposed model. In particular, IAN achieves 77.23% mAP and 80.45% top-1 accuracy on CUHK-SYSU, which outperform the state-of-the-art by 1.7% and 1.85%, respectively.