Academic Journal
Discriminative and noise-robust embedding for zero-shot learning.
| Τίτλος: | Discriminative and noise-robust embedding for zero-shot learning. |
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| Συγγραφείς: | Lei Y; organization=School of Telecommunications Engineering, Xidian University, city=Shaanxi, postcode=710071, country=China., Deng C; organization=School of Telecommunications Engineering, Xidian University, city=Shaanxi, postcode=710071, country=China., Duan Y; organization=School of Telecommunications Engineering, Xidian University, city=Shaanxi, postcode=710071, country=China., Gao Q; organization=School of Telecommunications Engineering, Xidian University, city=Shaanxi, postcode=710071, country=China. Electronic address: qxgao@xidian.edu.cn., Wang J; organization=School of Telecommunications Engineering, Xidian University, city=Shaanxi, postcode=710071, country=China. |
| Πηγή: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Oct; Vol. 202, pp. 109007. Date of Electronic Publication: 2026 Apr 22. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York : Pergamon Press, [c1988- |
| Ιατρικοί όροι (MeSH): | Neural Networks, Computer* , Machine Learning*, Humans ; Algorithms ; Representation Machine Learning |
| Περίληψη: | Zero-shot learning (ZSL) seeks to empower models with the ability to identify novel classes by transferring knowledge from previously encountered categories. Despite promising progress, most current ZSL methods rely heavily on pre-trained backbone networks for visual feature extraction. These features often contain distracting components that diminish the distinctiveness of critical information, thereby undermining knowledge transfer effectiveness. Moreover, existing methods typically overlook the impact of noisy or irrelevant details embedded in attribute representations. To address these limitations, we propose Discriminative and Noise-Robust Embedding (DNRE) for ZSL. It incorporates two major components: (1) a Channel Covariance Adaptive Enhancement (CCAE) module that captures higher-order dependencies and dynamically emphasizes informative channels to refine visual representations; and (2) a Dynamic Calibration Mechanism (DCM) that improves the alignment between attributes and local visual regions by suppressing noise and irrelevant signals. Comprehensive experiments on three widely-used ZSL benchmarks demonstrate that our method consistently surpasses state-of-the-art baselines, particularly in terms of accurate and robust attribute localization. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Dynamic calibration; Feature enhancement; Knowledge transfer; Zero-shot learning |
| Entry Date(s): | Date Created: 20260428 Date Completed: 20260718 Latest Revision: 20260718 |
| Update Code: | 20260718 |
| DOI: | 10.1016/j.neunet.2026.109007 |
| PMID: | 42048864 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2026.109007 |