Deep Group-shuffling Random Walk for Person Re-identification

TL;DR

This paper introduces a deep neural network that effectively incorporates gallery-to-gallery affinities into person re-identification, improving accuracy by end-to-end training and feature grouping.

Contribution

It proposes a novel group-shuffling random walk network that utilizes G2G affinities during training and testing, enhancing person re-identification performance.

Findings

Outperforms state-of-the-art on Market-1501, CUHK03, DukeMTMC datasets

Effectively integrates G2G affinities into deep learning framework

Achieves significant accuracy improvements over existing methods

Abstract

Person re-identification aims at finding a person of interest in an image gallery by comparing the probe image of this person with all the gallery images. It is generally treated as a retrieval problem, where the affinities between the probe image and gallery images (P2G affinities) are used to rank the retrieved gallery images. However, most existing methods only consider P2G affinities but ignore the affinities between all the gallery images (G2G affinity). Some frameworks incorporated G2G affinities into the testing process, which is not end-to-end trainable for deep neural networks. In this paper, we propose a novel group-shuffling random walk network for fully utilizing the affinity information between gallery images in both the training and testing processes. The proposed approach aims at end-to-end refining the P2G affinities based on G2G affinity information with a simple yet effective matrix operation, which can be integrated into deep neural networks. Feature grouping and group shuffle are also proposed to apply rich supervisions for learning better person features. The proposed approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets by large margins, which demonstrate the effectiveness of our approach.