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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 11/2023

09.07.2023

Attribute-Image Person Re-identification via Modal-Consistent Metric Learning

verfasst von: Jianqing Zhu, Liu Liu, Yibing Zhan, Xiaobin Zhu, Huanqiang Zeng, Dacheng Tao

Erschienen in: International Journal of Computer Vision | Ausgabe 11/2023

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Abstract

Attribute-image person re-identification (AIPR) is a cross-modal retrieval task that searches person images who meet a list of attributes. Due to large modal gaps between attributes and images, current AIPR methods generally depend on cross-modal feature alignment, but they do not pay enough attention to similarity metric jitters among varying modal configurations (i.e., attribute probe vs. image gallery, image probe vs. attribute gallery, image probe vs. image gallery, and attribute probe vs. attribute gallery). In this paper, we propose a modal-consistent metric learning (MCML) method that stably measures comprehensive similarities between attributes and images. Our MCML is with favorable properties that differ in two significant ways from previous methods. First, MCML provides a complete multi-modal triplet (CMMT) loss function that pulls the distance between the farthest positive pair as close as possible while pushing the distance between the nearest negative pair as far as possible, independent of their modalities. Second, MCML develops a modal-consistent matching regularization (MCMR) to reduce the diversity of matching matrices and guide consistent matching behaviors on varying modal configurations. Therefore, our MCML integrates the CMMT loss function and MCMR, requiring no complex cross-modal feature alignments. Theoretically, we offer the generalization bound to establish the stability of our MCML model by applying on-average stability. Experimentally, extensive results on PETA and Market-1501 datasets show that the proposed MCML is superior to the state-of-the-art approaches.
Vorheriger Artikel Deep Unfolding for Snapshot Compressive Imaging
Nächster Artikel A Dynamic Feature Interaction Framework for Multi-task Visual Perception

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Fußnoten
1
We use \(i \in [ m ]\) to denote that i is generated from \([ m ] = \{ {1,2,...,m} \}\). The same definition is also applied to \(l_i\!\in \! [c].\)
 
2
The single-modal HMT loss function means only images are applied to the HMT loss function, while the cross-modal HMT loss function means both images and attributes are applied to the HMT loss function.
 
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Metadaten
Titel
Attribute-Image Person Re-identification via Modal-Consistent Metric Learning
verfasst von
Jianqing Zhu
Liu Liu
Yibing Zhan
Xiaobin Zhu
Huanqiang Zeng
Dacheng Tao
Publikationsdatum
09.07.2023
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 11/2023
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-023-01841-7

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