Zero-shot image classification based on class representation learning and attribute embedding learning.

ABSTRACT: Zero-shot learning (ZSL) aims to classify unseen classes by leveraging semantic information from seen classes, addressing the challenge of limited labeled data. In recent years, ZSL methods have focused on extracting attribute-level features from images and aligning them with semantic features within an embedding space. However, existing approaches often fail to account for significant visual variations within the same attribute, leading to noisy attribute-level features that degrade classification performance.To tackle these challenges, we propose a novel zero-shot image classification method named CRAE (Class Representation and Attribute Embedding), which combines class representation learning and attribute embedding learning to enhance classification robustness and accuracy. Specificall

SUBMITTER: Shen H 

PROVIDER: S-EPMC12622808 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature