Exploring Cross-Modality Commonalities via Dual-Stream Multi-Branch Network for Infrared-Visible Person Re-Identification Article Swipe
Cheng Ding
,
Xiaohong Li
,
Meibin Qi
,
Xueliang Liu
,
Cuiqun Chen
,
Dawei Niu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2966002
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2966002
Infrared-Visible person Re-IDentification (IV-ReID) is an emerging subject, which has important research significance for nighttime monitoring. Existing works focus on reducing cross-modality discrepancies, but the cross-modality discrepancy cannot be completely eliminated. Therefore, we concentrate on excavating cross-modality commonalities to handle the task. Since similar features between two modalities are possessed of cross-modality commonalities, our goal is to find more similar features in infrared and visible images. A novel Dual-stream Multi-layer Corresponding Fusion Network(DMCF) is proposed to explore more similar features between two modalities in this paper. It mainly contains three aspects. 1) We explore more similar features between two modalities by learning low-level features, Meanwhile, we also propose a method that the same level features between two modalities are correspondingly fused to reduce cross-modality discrepancies. 2) We adopt different Multi-granularity dividing methods for multi-layer features, so that it can improve the ability of the model to perceive feature details. 3) We separately calculate the loss for different-layer features. Therefore, we learn different weighting factors for the loss of different hierarchical features through Multi-task Learning, so that each branch can be fully optimized. Extensive experiments on two datasets demonstrate the superior performance compared to the state-of-the-arts.
Related Topics
Concepts
Modality (human–computer interaction)
Computer science
Modalities
Weighting
Artificial intelligence
Feature (linguistics)
Task (project management)
Identification (biology)
Pattern recognition (psychology)
Machine learning
Social science
Management
Philosophy
Medicine
Radiology
Sociology
Biology
Botany
Economics
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.2966002
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/08957128.pdf
- OA Status
- gold
- Cited By
- 18
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3000690231
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3000690231Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2020.2966002Digital Object Identifier
- Title
-
Exploring Cross-Modality Commonalities via Dual-Stream Multi-Branch Network for Infrared-Visible Person Re-IdentificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Cheng Ding, Xiaohong Li, Meibin Qi, Xueliang Liu, Cuiqun Chen, Dawei NiuList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.2966002Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/08957128.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/08957128.pdfDirect OA link when available
- Concepts
-
Modality (human–computer interaction), Computer science, Modalities, Weighting, Artificial intelligence, Feature (linguistics), Task (project management), Identification (biology), Pattern recognition (psychology), Machine learning, Social science, Management, Philosophy, Medicine, Radiology, Sociology, Biology, Botany, Economics, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 6, 2022: 4, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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