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Unsupervised Person Re-identification via Multi-Label Prediction and Classification based on Graph-Structural Insight

2021-06-16 14:00:40
Jongmin Yu, Hyeontaek Oh

Abstract

This paper addresses unsupervised person re-identification (Re-ID) using multi-label prediction and classification based on graph-structural insight. Our method extracts features from person images and produces a graph that consists of the features and a pairwise similarity of them as nodes and edges, respectively. Based on the graph, the proposed graph structure based multi-label prediction (GSMLP) method predicts multi-labels by considering the pairwise similarity and the adjacency node distribution of each node. The multi-labels created by GSMLP are applied to the proposed selective multi-label classification (SMLC) loss. SMLC integrates a hard-sample mining scheme and a multi-label classification. The proposed GSMLP and SMLC boost the performance of unsupervised person Re-ID without any pre-labelled dataset. Experimental results justify the superiority of the proposed method in unsupervised person Re-ID by producing state-of-the-art performance. The source code for this paper is publicly available on 'this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2106.08798

PDF

https://arxiv.org/pdf/2106.08798.pdf


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