Deep balanced discrete hashing for image retrieval
Open accessIntegrity 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?
DSH and DBDH are Retrieval Oriented hash schemes.
“DSH and DBDH are Retrieval Oriented hash schemes.”
From Deep Perceptual Hash Based on Hash Center for Image Copyright Protection (2022) · cites this paper as [44]
Our scheme is significantly better than RP-IVD and DBDH.
“This means that our scheme is signi cantly better than RP-IVD [20] and DBDH [44].”
From Deep Perceptual Hash Based on Hash Center for Image Copyright Protection (2022) · cites this paper as [44]
The citation lists haven’t been fetched yet
References 72 · Cited by 83
- Authors
- Zheng, X., Zhang, Y., & Lu, X.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Lu, Xiaoqiang
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 403
- Pages
- 224-236
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W3020188837
- Cited by
- 83
- References
- 72
- Citations by year
- 2026: 5 · 2025: 17 · 2024: 16 · 2023: 18 · 2022: 16 · 2021: 9 · 2020: 2
- FWCI
- 4.37
- Type
- journal article
- Language
- en
- Open access
- green
- ir.opt.ac.cn
- Licence
- Locations
- Neurocomputing (publishedVersion)
- Institutional Repository of Xi'an Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (Xian Institute of Optics and Precision Mechanics) (submittedVersion, cc-by-nc-sa)
- Funders
- National Natural Science Foundation of China (61806193, 61702498, 61772510)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- deep hashing, image retrieval, straight-through estimator, quantization error, binary codes, pairwise loss, label consistency, supervised hashing
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.
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