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However, the opaque nature of neural networks makes it challenging to discern the knowledge learned by the model, and existing methods often lack explainability, limiting their reliable application in high-stakes fields such as medical diagnosis and autonomous driving. To address this, we propose a visually explainable dynamic similarity network (VEDSNet), which achieves a balance of performance, explainability, and efficiency through a lightweight architecture (approximately 6.8M parameters, built on a ViT-Tiny backbone). The Feature Decomposition Module (FDM) generates fine-grained, semantically meaningful representations via parallel feature learning, providing intuitive visual insights into the model\u2019s decisions. The Dynamic Metric Module (DMM) employs a sample-adaptive dual-metric strategy to enhance discrimination with limited data, switching to a single metric for efficiency when data is sufficient. Experiments on standard datasets demonstrate that VEDSNet achieves high classification accuracy while providing clear visual explanations of its decision-making process, making it suitable for efficient deployment in resource-constrained scenarios.<\/jats:p>","DOI":"10.2478\/jaiscr-2026-0012","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T08:56:26Z","timestamp":1772528186000},"page":"237-256","source":"Crossref","is-referenced-by-count":2,"title":["A Visually Explainable Dynamic Similarity Network for Few-Shot Classification"],"prefix":"10.2478","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4657-3893","authenticated-orcid":false,"given":"Zirui","family":"Pei","sequence":"first","affiliation":[{"name":"College of Computer, Electronics and Information , Guangxi University Nanning , , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4911-6958","authenticated-orcid":false,"given":"Zuqiang","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Computer, Electronics and Information , Guangxi University , Nanning , , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0371-0823","authenticated-orcid":false,"given":"Tingting","family":"Diao","sequence":"additional","affiliation":[{"name":"College of Computer, Electronics and Information , Guangxi University , Nanning , , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2108-5025","authenticated-orcid":false,"given":"Peng","family":"Miao","sequence":"additional","affiliation":[{"name":"College of Computer, Electronics and Information , Guangxi University , Nanning , , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0782-4880","authenticated-orcid":false,"given":"Yifan","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Computer, Electronics and Information , Guangxi University , Nanning , , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4117-731X","authenticated-orcid":false,"given":"Chaohong","family":"Tan","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Digital Infrastructure, Guangxi Zhuang Autonomous Region Information Center Nanning , , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"2026030310273135875_j_jaiscr-2026-0012_ref_001","doi-asserted-by":"crossref","unstructured":"F. 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