# MovieNet-PS: A Large-Scale Person Search Dataset in the Wild

arXiv: https://arxiv.org/abs/2112.02500 · HTML: https://mlsanity.com/paper/2112.02500/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 5 Dec 2021.

## Builds on (most cited first, 13 of 13)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [MovieNet: A Holistic Dataset for Movie Understanding](https://mlsanity.com/paper/2007.10937/) (2020)
- [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)
- [Learning Context Graph for Person Search](https://mlsanity.com/paper/1904.01830/) (2019)
- [Anchor-Free Person Search](https://mlsanity.com/paper/2103.11617/) (2021)
- [Query-guided End-to-End Person Search](https://mlsanity.com/paper/1905.01203/) (2019)
- [Sequential End-to-end Network for Efficient Person Search](https://mlsanity.com/paper/2103.10148/) (2021)
- [Person Search by Multi-Scale Matching](https://mlsanity.com/paper/1807.08582/) (2018)
- [Robust Partial Matching for Person Search in the Wild](https://mlsanity.com/paper/2004.09329/) (2020)
- [Diverse Knowledge Distillation for End-to-End Person Search](https://mlsanity.com/paper/2012.11187/) (2020)
- [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)

## Built on by (most cited first, 4 of 4)

- [PLIP: Language-Image Pre-training for Person Representation Learning](https://mlsanity.com/paper/2305.08386/) (2023)
- [Prompting Continual Person Search](https://mlsanity.com/paper/2410.19239/) (2024)
- [PS-ReID: Advancing Person Re-Identification and Precise Segmentation with Multimodal Retrieval](https://mlsanity.com/paper/2503.21595/) (2025)
- [Lifelong Person Search](https://mlsanity.com/paper/2407.21252/) (2024)

## Abstract (arXiv)

Person search aims to jointly localize and identify a query person from natural, uncropped images, which has been actively studied over the past few years. In this paper, we delve into the rich context information globally and locally surrounding the target person, which we refer to as scene and group context, respectively. Unlike previous works that treat the two types of context individually, we exploit them in a unified global-local context network (GLCNet) with the intuitive aim of feature enhancement. Specifically, re-ID embeddings and context features are simultaneously learned in a multi-stage fashion, ultimately leading to enhanced, discriminative features for person search. We conduct the experiments on two person search benchmarks (i.e., CUHK-SYSU and PRW) as well as extend our approach to a more challenging setting (i.e., character search on MovieNet). Extensive experimental results demonstrate the consistent improvement of the proposed GLCNet over the state-of-the-art methods on all three datasets. Our source codes, pre-trained models, and the new dataset are publicly available at: https://github.com/ZhengPeng7/GLCNet.