# Group Re-Identification with Multi-grained Matching and Integration

arXiv: https://arxiv.org/abs/1905.07108 · HTML: https://mlsanity.com/paper/1905.07108/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 8 of 8)

- [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024)
- [Learning Multi-Attention Context Graph for Group-Based Re-Identification](https://mlsanity.com/paper/2104.14236/) (2021)
- [MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR](https://mlsanity.com/paper/2312.02409/) (2023)
- [SAPI: Surroundings-Aware Vehicle Trajectory Prediction at Intersections](https://mlsanity.com/paper/2306.01812/) (2023)
- [Vision Transformer based Random Walk for Group Re-Identification](https://mlsanity.com/paper/2410.05808/) (2024)
- [The Research of Group Re-identification from Multiple Cameras](https://mlsanity.com/paper/2407.14620/) (2024)
- [Group-CLIP Uncertainty Modeling for Group Re-Identification](https://mlsanity.com/paper/2502.06460/) (2025)
- [Find Them All: Unveiling MLLMs for Versatile Person Re-identification](https://mlsanity.com/paper/2508.06908/) (2025)

## Abstract (arXiv)

The task of re-identifying groups of people underdifferent camera views is an important yet less-studied problem.Group re-identification (Re-ID) is a very challenging task sinceit is not only adversely affected by common issues in traditionalsingle object Re-ID problems such as viewpoint and human posevariations, but it also suffers from changes in group layout andgroup membership. In this paper, we propose a novel conceptof group granularity by characterizing a group image by multi-grained objects: individual persons and sub-groups of two andthree people within a group. To achieve robust group Re-ID,we first introduce multi-grained representations which can beextracted via the development of two separate schemes, i.e. onewith hand-crafted descriptors and another with deep neuralnetworks. The proposed representation seeks to characterize bothappearance and spatial relations of multi-grained objects, and isfurther equipped with importance weights which capture varia-tions in intra-group dynamics. Optimal group-wise matching isfacilitated by a multi-order matching process which in turn,dynamically updates the importance weights in iterative fashion.We evaluated on three multi-camera group datasets containingcomplex scenarios and large dynamics, with experimental resultsdemonstrating the effectiveness of our approach. The published dataset can be found in urlhttp://min.sjtu.edu.cn/lwydemo/GroupReID.html