# Making Person Search Enjoy the Merits of Person Re-identification

arXiv: https://arxiv.org/abs/2108.10536 · HTML: https://mlsanity.com/paper/2108.10536/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 54 references that are in the map.
- First posted on arXiv 24 Aug 2021.

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

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [Joint Detection and Identification Feature Learning for Person Search](https://mlsanity.com/paper/1604.01850/) (2016)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [Person Re-identification in the Wild](https://mlsanity.com/paper/1604.02531/) (2016)
- [A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification](https://mlsanity.com/paper/1906.08332/) (2019)
- [Relation-Aware Global Attention for Person Re-identification](https://mlsanity.com/paper/1904.02998/) (2019)
- [Learning Context Graph for Person Search](https://mlsanity.com/paper/1904.01830/) (2019)
- [Person Search via A Mask-Guided Two-Stream CNN Model](https://mlsanity.com/paper/1807.08107/) (2018)
- [Anchor-Free Person Search](https://mlsanity.com/paper/2103.11617/) (2021)
- [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)
- [Neural Person Search Machines](https://mlsanity.com/paper/1707.06777/) (2017)
- [Region-based Quality Estimation Network for Large-scale Person Re-identification](https://mlsanity.com/paper/1711.08766/) (2017)
- [Beyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss](https://mlsanity.com/paper/2009.10295/) (2020)
- [Robust Partial Matching for Person Search in the Wild](https://mlsanity.com/paper/2004.09329/) (2020)
- [IAN: The Individual Aggregation Network for Person Search](https://mlsanity.com/paper/1705.05552/) (2017)
- [Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search](https://mlsanity.com/paper/2102.10795/) (2021)
- [Diverse Knowledge Distillation for End-to-End Person Search](https://mlsanity.com/paper/2012.11187/) (2020)
- [Knowledge Distillation for End-to-End Person Search](https://mlsanity.com/paper/1909.01058/) (2019)
- [Weighted Bilinear Coding over Salient Body Parts for Person Re-identification](https://mlsanity.com/paper/1803.08580/) (2018)
- [Distribution Context Aware Loss for Person Re-identification](https://mlsanity.com/paper/1911.07273/) (2019)

## Built on by (most cited first, 3 of 3)

- [Query-Guided Networks for Few-shot Fine-grained Classification and Person Search](https://mlsanity.com/paper/2209.10250/) (2022)
- [Harnessing Weak Pair Uncertainty for Text-based Person Search](https://mlsanity.com/paper/2604.08877/) (2026)
- [Subtask-dominated Transfer Learning for Long-tail Person Search](https://mlsanity.com/paper/2112.00527/) (2021)

## Abstract (arXiv)

Person search is an extended task of person re-identification (Re-ID). However, most existing one-step person search works have not studied how to employ existing advanced Re-ID models to boost the one-step person search performance due to the integration of person detection and Re-ID. To address this issue, we propose a faster and stronger one-step person search framework, the Teacher-guided Disentangling Networks (TDN), to make the one-step person search enjoy the merits of the existing Re-ID researches. The proposed TDN can significantly boost the person search performance by transferring the advanced person Re-ID knowledge to the person search model. In the proposed TDN, for better knowledge transfer from the Re-ID teacher model to the one-step person search model, we design a strong one-step person search base framework by partially disentangling the two subtasks. Besides, we propose a Knowledge Transfer Bridge module to bridge the scale gap caused by different input formats between the Re-ID model and one-step person search model. During testing, we further propose the Ranking with Context Persons strategy to exploit the context information in panoramic images for better retrieval. Experiments on two public person search datasets demonstrate the favorable performance of the proposed method.