Cross-domain person re-identification using Dual Generation Learning in camera sensor networks
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Zhang et al. propose a style translation-based method for cross-domain person re-identification in camera sensor networks that can reduce domain shift and learn domain-invariant features.
“To address it, Zhang et al. propose a style translation-based method for cross-domain person re-identification in camera sensor networks [206], which can reduce domain shift and learn domain-invariant”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [206]
The citation lists haven’t been fetched yet
References 32 · Cited by 5
- Authors
- Zhang, Z., Wang, Y., & Liu, S.
- Affiliations
- Tianjin Normal University (CN)
- Corresponding author
- Zhang, Zhong
- Venue
- Ad Hoc Networks
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 97
- Pages
- 102019
- ISSN
- 1570-8705 (ISSN-L)
- OpenAlex
- W2980009027
- Cited by
- 5
- References
- 32
- Citations by year
- 2024: 1 · 2023: 1 · 2021: 1 · 2020: 2
- FWCI
- 0.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ad Hoc Networks (publishedVersion)
- Funders
- Tianjin Normal University
- Natural Science Foundation of Tianjin City
- National Laboratory of Pattern Recognition
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- multi-camera systems, domain shift, triplet loss, representation learning
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
