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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/60622
Title: Methods of low data image classification with neural network
Authors: Chen, Y. M.
Keywords: материалы конференций;Image classification;Deep learning;Zero-shot learning;Few-shot learning;CLIP;Convolutional Neural Networks
Issue Date: 2025
Publisher: БГУИР
Citation: Chen, Y. M. Methods of low data image classification with neural network / Y. M. Chen // Технологии передачи и обработки информации : материалы Международного научно-технического семинара, Минск, апрель 2025 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: В. Ю. Цветков [и др.]. – Минск, 2025. – С. 162–164.
Abstract: Image classification has evolved from relying on handcrafted feature engineering to a data-driven deep learning paradigm, with recent breakthroughs extending its capabilities to zeroshot and few-shot learning scenarios. This paper introduces emerging paradigms for low-data scenarios: zero-shot learning methods, including multimodal models (e.g., CLIP) that align visual and textual embeddings for open vocabulary classification and generative frameworks for synthesizing features of unseen classes; and (2) few-shot learning strategies, such as meta-learning (e.g., MAML), metric-based networks (e.g., ProtoNet), and fast adaptation techniques (e.g., Tip-Adapter) that leverage pre-trained knowledge for fast adaptation. While these methods reduce the reliance on labeled data, challenges remain in domain adaptation, fine-grained classification, and computational efficiency.
URI: https://libeldoc.bsuir.by/handle/123456789/60622
Appears in Collections:Технологии передачи и обработки информации : материалы Международного научно-технического семинара (2025)

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