The DOI matches its Crossref record.
Cross-Linked Unified Embedding for Cross-Modality Representation Learning
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
Tu et al. introduced the CLUE model for utilizing cross-encoders between modalities to learn a comprehensive representation of incomplete datasets.
“Tu et al. introduced the cross-linked unified embedding (CLUE) model for utilizing the cross-encoders between modalities to learn a comprehensive representation of incomplete datasets [152].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [152]
Citations
Cited by
Sorted by citations
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75
Metadata
- Authors
- Tu, X., Cao, Z.-J., Chenrui, X., Mostafavi, S., & Gao, G.
- Venue
- Advances in Neural Information Processing Systems 35
- Published
- 2022
- Pages
- 15942-15955
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133187319
- Cited by
- 5
- References
- 0
- Citations by year
- 2026: 5
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- cross-modal embeddings, crosslinking
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 ›
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