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Enhancing Zero-Shot Learning Through Kernelized Visual Prototypes and Similarity Learning

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Publicado en:Mathematics vol. 13, no. 3 (2025), p. 412
Autor principal: Cheng, Kanglong
Otros Autores: Bowen, Fang
Publicado:
MDPI AG
Materias:
Similarity
Visual discrimination
Research methodology
Teaching methods
Zero-shot learning
Classification
Maps
Prototypes
Image classification
Image acquisition
Similarity measures
Distance learning
Semantics
Acceso en línea:Citation/Abstract
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Resumen:Zero-shot learning (ZSL) holds significant promise for scaling image classification to previously unseen classes by leveraging previously acquired knowledge. However, conventional ZSL methods face challenges such as domain-shift and hubness problems. To address these issues, we propose a novel kernelized similarity learning approach that reduces intraclass similarity while increasing interclass similarity. Specifically, we utilize kernelized ridge regression to learn visual prototypes for unseen classes in the semantic vectors. Furthermore, we introduce kernel polarization and autoencoder structures into the similarity function to enhance discriminative ability and mitigate the hubness and domain-shift problems. Extensive experiments on five benchmark datasets demonstrate that our method outperforms state-of-the-art ZSL and generalized zero-shot learning (GZSL) methods, highlighting its effectiveness in improving classification performance for unseen classes.
ISSN:2227-7390
DOI:10.3390/math13030412
Fuente:Engineering Database