# Diverse Knowledge Distillation for End-to-End Person Search

arXiv: https://arxiv.org/abs/2012.11187 · HTML: https://mlsanity.com/paper/2012.11187/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 16 of 16)

- [PSTR: End-to-End One-Step Person Search With Transformers](https://mlsanity.com/paper/2204.03340/) (2022)
- [Weakly Supervised Person Search with Region Siamese Networks](https://mlsanity.com/paper/2109.06109/) (2021)
- [Text-based Person Search in Full Images via Semantic-Driven Proposal Generation](https://mlsanity.com/paper/2109.12965/) (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)
- [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)
- [MovieNet-PS: A Large-Scale Person Search Dataset in the Wild](https://mlsanity.com/paper/2112.02500/) (2021)
- [Making Person Search Enjoy the Merits of Person Re-identification](https://mlsanity.com/paper/2108.10536/) (2021)
- [CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search](https://mlsanity.com/paper/2203.14307/) (2022)
- [Query-Guided Networks for Few-shot Fine-grained Classification and Person Search](https://mlsanity.com/paper/2209.10250/) (2022)
- [PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement](https://mlsanity.com/paper/2309.11125/) (2023)
- [PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search](https://mlsanity.com/paper/2210.03433/) (2022)
- [LEAPS: End-to-End One-Step Person Search With Learnable Proposals](https://mlsanity.com/paper/2303.11859/) (2023)
- [Subtask-dominated Transfer Learning for Long-tail Person Search](https://mlsanity.com/paper/2112.00527/) (2021)
- [Lifelong Person Search](https://mlsanity.com/paper/2407.21252/) (2024)

## Abstract (arXiv)

Person search aims to localize and identify a specific person from a gallery of images. Recent methods can be categorized into two groups, i.e., two-step and end-to-end approaches. The former views person search as two independent tasks and achieves dominant results using separately trained person detection and re-identification (Re-ID) models. The latter performs person search in an end-to-end fashion. Although the end-to-end approaches yield higher inference efficiency, they largely lag behind those two-step counterparts in terms of accuracy. In this paper, we argue that the gap between the two kinds of methods is mainly caused by the Re-ID sub-networks of end-to-end methods. To this end, we propose a simple yet strong end-to-end network with diverse knowledge distillation to break the bottleneck. We also design a spatial-invariant augmentation to assist model to be invariant to inaccurate detection results. Experimental results on the CUHK-SYSU and PRW datasets demonstrate the superiority of our method against existing approaches -- it achieves on par accuracy with state-of-the-art two-step methods while maintaining high efficiency due to the single joint model. Code is available at: https://git.io/DKD-PersonSearch.