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Related Experiment Video

Updated: Aug 28, 2025

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
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Not All Instances Contribute Equally: Instance-Adaptive Class Representation Learning for Few-Shot Visual

Mengya Han, Yibing Zhan, Yong Luo

    IEEE Transactions on Neural Networks and Learning Systems
    |September 22, 2022
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    Summary

    This study introduces an instance-adaptive class representation learning network (ICRL-Net) to improve few-shot visual recognition by assigning adaptive weights to instances, overcoming biased class representations for better accuracy.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Few-shot visual recognition aims to identify new visual concepts using minimal labeled data.
    • Metric-based meta-learning methods often suffer from biased class representations due to treating all instances equally.

    Purpose of the Study:

    • To develop a novel framework, the instance-adaptive class representation learning network (ICRL-Net), for enhanced few-shot visual recognition.
    • To address the issue of biased class representations in metric-based meta-learning.

    Main Methods:

    • Proposes an adaptive instance revaluing network (AIRN) to learn instance significance weights.
    • Introduces an improved bilinear instance representation.
    • Incorporates intraclass instance clustering and interclass representation distinguishing losses.

    Main Results:

    • The proposed ICRL-Net significantly improves performance on few-shot visual recognition tasks.
    • Experimental results on miniImageNet, tieredImageNet, CIFAR-FS, and FC100 benchmarks demonstrate superiority over state-of-the-art methods.

    Conclusions:

    • The ICRL-Net effectively mitigates biased class representations by adaptively revaluing instances.
    • The novel approach enhances the accuracy and robustness of few-shot visual recognition systems.