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Related Experiment Videos

Semantic-Guided Class-Imbalance Learning Model for Zero-Shot Image Classification.

Zhong Ji, Xuejie Yu, Yunlong Yu

    IEEE Transactions on Cybernetics
    |May 27, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a sample-balanced training method to improve zero-shot image classification (ZSIC) performance on imbalanced datasets. The approach ensures equal contribution from all classes, enhancing recognition of unseen categories.

    Related Experiment Videos

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Zero-shot image classification (ZSIC) aims to recognize images from unseen classes, but struggles with real-world class imbalance.
    • Class imbalance, common in real-world data, hinders knowledge transfer from sample-scarce seen classes to unseen classes in traditional ZSIC.
    • This degradation in generalization ability is a significant challenge for ZSIC systems.

    Purpose of the Study:

    • To address the class-imbalance issue in zero-shot image classification (ZSIC).
    • To enhance the generalization ability of ZSIC models, particularly for unseen categories related to sample-scarce seen categories.
    • To propose a novel training process and feature fusion model for improved ZSIC.

    Main Methods:

    • Implemented a sample-balanced training process, ensuring equal image selection from each class per batch.
    • Developed an efficient semantic-guided feature fusion model to create discriminative class visual prototypes.
    • Weighted selected samples based on class representativeness for improved visual-semantic interaction.

    Main Results:

    • Achieved promising results on three imbalanced ZSIC benchmark datasets for both traditional and generalized ZSIC tasks.
    • Demonstrated significant improvements for unseen categories closely related to sample-scarce seen categories.
    • Showcased performance gains against baseline models on two class-balanced datasets.

    Conclusions:

    • The proposed sample-balanced training and semantic-guided feature fusion effectively alleviate class imbalance in ZSIC.
    • The approach enhances the recognition of unseen categories, especially those linked to data-scarce classes.
    • This method offers a robust solution for improving ZSIC performance in real-world, imbalanced scenarios.