Unsupervised person re-identification with multi-label learning guided self-paced clustering
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NLG emphasizes training models to generate coherent, contextually-relevant, and creative text responses, critical in chatbots, virtual assistants, and automated content creation tools.
“NLG emphasizes the training of models to generate coherent, contextually-relevant, and creative text responses, a critical component in chatbots, virtual assistants, and automated content creation tools [199], [36], [200], [201].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [199]
Cites
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McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“• Unsupervised Learning: Unsupervised learning is essential in AI for uncovering patterns within unlabeled data, a process central to tasks like feature learning and clustering [169], [170].”
Background
- Authors
- Li, Q., Peng, X., Qiao, Y., & Hao, Q.
- Affiliations
- Southern University of Science and Technology (CN)
- Shenzhen Technology University (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Peng, Xiaojiang
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 125
- Pages
- 108521
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W4226090797
- Cited by
- 35
- References
- 19
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 9 · 2023: 8 · 2022: 12
- FWCI
- 2.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Pattern Recognition (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- unsupervised person re-identification, multi-label classification, multi-scale feature learning, pseudo-labeling
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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