Unsupervised Person Re-identification via Multi-Label Prediction and
Classification based on Graph-Structural Insight
- URL: http://arxiv.org/abs/2106.08798v1
- Date: Wed, 16 Jun 2021 14:00:40 GMT
- Title: Unsupervised Person Re-identification via Multi-Label Prediction and
Classification based on Graph-Structural Insight
- Authors: Jongmin Yu and Hyeontaek Oh
- Abstract summary: 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.
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.
- Score: 1.7894377200944507
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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 'https://github.com/uknownpioneer/GSMLP-SMLC.git'.
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