# Sequential End-to-end Network for Efficient Person Search

arXiv: https://arxiv.org/abs/2103.10148 · HTML: https://mlsanity.com/paper/2103.10148/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Joint Detection and Identification Feature Learning for Person Search](https://mlsanity.com/paper/1604.01850/) (2016)
- [Person Re-identification in the Wild](https://mlsanity.com/paper/1604.02531/) (2016)
- [Horizontal Pyramid Matching for Person Re-identification](https://mlsanity.com/paper/1804.05275/) (2018)
- [Learning Context Graph for Person Search](https://mlsanity.com/paper/1904.01830/) (2019)
- [Object detection via a multi-region & semantic segmentation-aware CNN model](https://mlsanity.com/paper/1505.01749/) (2015)
- [Spatial-Temporal Person Re-identification](https://mlsanity.com/paper/1812.03282/) (2018)
- [Person Search via A Mask-Guided Two-Stream CNN Model](https://mlsanity.com/paper/1807.08107/) (2018)
- [Re-ID Driven Localization Refinement for Person Search](https://mlsanity.com/paper/1909.08580/) (2019)
- [Query-guided End-to-End Person Search](https://mlsanity.com/paper/1905.01203/) (2019)
- [Person Search by Multi-Scale Matching](https://mlsanity.com/paper/1807.08582/) (2018)
- [Deformable Part Models are Convolutional Neural Networks](https://mlsanity.com/paper/1409.5403/) (2014)
- [Neural Person Search Machines](https://mlsanity.com/paper/1707.06777/) (2017)
- [IAN: The Individual Aggregation Network for Person Search](https://mlsanity.com/paper/1705.05552/) (2017)
- [Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization](https://mlsanity.com/paper/1606.04446/) (2016)

## Built on by (most cited first, 30 of 38)

- [Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks](https://mlsanity.com/paper/2303.17602/) (2023)
- [PLIP: Language-Image Pre-training for Person Representation Learning](https://mlsanity.com/paper/2305.08386/) (2023)
- [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)
- [Exploring Visual Context for Weakly Supervised Person Search](https://mlsanity.com/paper/2106.10506/) (2021)
- [OIMNet++: Prototypical Normalization and Localization-aware Learning for Person Search](https://mlsanity.com/paper/2207.10320/) (2022)
- [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)
- [Context-Aware Unsupervised Clustering for Person Search](https://mlsanity.com/paper/2110.01341/) (2021)
- [Efficient Person Search: An Anchor-Free Approach](https://mlsanity.com/paper/2109.00211/) (2021)
- [MovieNet-PS: A Large-Scale Person Search Dataset in the Wild](https://mlsanity.com/paper/2112.02500/) (2021)
- [Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Search](https://mlsanity.com/paper/2302.04607/) (2023)
- [Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching](https://mlsanity.com/paper/2506.12413/) (2025)
- [CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search](https://mlsanity.com/paper/2203.14307/) (2022)
- [PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement](https://mlsanity.com/paper/2309.11125/) (2023)
- [Domain Generalizable Person Search Using Unreal Dataset](https://mlsanity.com/paper/2404.00626/) (2024)
- [Scene-Adaptive Person Search via Bilateral Modulations](https://mlsanity.com/paper/2405.02834/) (2024)
- [Prompting Continual Person Search](https://mlsanity.com/paper/2410.19239/) (2024)
- [Head and Body: Unified Detector and Graph Network for Person Search in Media](https://mlsanity.com/paper/2111.13888/) (2021)
- [Grouped Adaptive Loss Weighting for Person Search](https://mlsanity.com/paper/2209.11492/) (2022)
- [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)
- [Unsupervised Domain Adaptive Person Search via Dual Self-Calibration](https://mlsanity.com/paper/2412.16506/) (2024)
- [Leveraging Prior Knowledge of Diffusion Model for Person Search](https://mlsanity.com/paper/2510.01841/) (2025)
- [Subtask-dominated Transfer Learning for Long-tail Person Search](https://mlsanity.com/paper/2112.00527/) (2021)

## Abstract (arXiv)

Person search aims at jointly solving Person Detection and Person Re-identification (re-ID). Existing works have designed end-to-end networks based on Faster R-CNN. However, due to the parallel structure of Faster R-CNN, the extracted features come from the low-quality proposals generated by the Region Proposal Network, rather than the detected high-quality bounding boxes. Person search is a fine-grained task and such inferior features will significantly reduce re-ID performance. To address this issue, we propose a Sequential End-to-end Network (SeqNet) to extract superior features. In SeqNet, detection and re-ID are considered as a progressive process and tackled with two sub-networks sequentially. In addition, we design a robust Context Bipartite Graph Matching (CBGM) algorithm to effectively employ context information as an important complementary cue for person matching. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method achieves state-of-the-art results. Also, our model runs at 11.5 fps on a single GPU and can be integrated into the existing end-to-end framework easily.