# Vision Transformer based Random Walk for Group Re-Identification

arXiv: https://arxiv.org/abs/2410.05808 · HTML: https://mlsanity.com/paper/2410.05808/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Learning Multi-Attention Context Graph for Group-Based Re-Identification](https://mlsanity.com/paper/2104.14236/) (2021)
- [Group Re-Identification with Multi-grained Matching and Integration](https://mlsanity.com/paper/1905.07108/) (2019)
- [Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding](https://mlsanity.com/paper/1707.09173/) (2017)

## Built on by (most cited first, 1 of 1)

- [Group-CLIP Uncertainty Modeling for Group Re-Identification](https://mlsanity.com/paper/2502.06460/) (2025)

## Abstract (arXiv)

Group re-identification (re-ID) aims to match groups with the same people under different cameras, mainly involves the challenges of group members and layout changes well. Most existing methods usually use the k-nearest neighbor algorithm to update node features to consider changes in group membership, but these methods cannot solve the problem of group layout changes. To this end, we propose a novel vision transformer based random walk framework for group re-ID. Specifically, we design a vision transformer based on a monocular depth estimation algorithm to construct a graph through the average depth value of pedestrian features to fully consider the impact of camera distance on group members relationships. In addition, we propose a random walk module to reconstruct the graph by calculating affinity scores between target and gallery images to remove pedestrians who do not belong to the current group. Experimental results show that our framework is superior to most methods.