Modality-transfer generative adversarial network and dual-level unified latent representation for visible thermal Person re-identification
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The generative adversarial learning paradigm has recently been widely adopted in multimodal arenas such as image captioning and image retrieval.
“In addition to its use in unimodal applications, the generative adversarial learning paradigm has recently been widely adopted in multimodal arenas, where two or more modalities are involved, such as image captioning [227] and image retrieval [228].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [228]
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Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
- Authors
- Fan, X., Jiang, W., Luo, H., & Mao, W.
- Affiliations
- State Key Laboratory of Industrial Control Technology (CN)
- Corresponding author
- Jiang, Wei
- Venue
- The Visual Computer
- Published
- 2020-11-24
- Volume
- 38
- Issue
- 1
- Pages
- 279-294
- ISSN
- 0178-2789 (ISSN-L)
- 1432-2315
- OpenAlex
- W3106715297
- Cited by
- 45
- References
- 55
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 16 · 2023: 12 · 2022: 8 · 2021: 2
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Visual Computer (publishedVersion)
- Funders
- National Natural Science Foundation of China (No. 61633019)
- Public Projects of Zhejiang Province (No. LGF18F030002)
- Science Foundation of Chinese Aerospace Industry (JCKY2018204B053)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- modality transfer, generative adversarial networks, near-infrared imagery
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