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Related Experiment Video

Updated: Dec 13, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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Label Independent Memory for Semi-Supervised Few-Shot Video Classification.

Linchao Zhu, Yi Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 6, 2020
    PubMed
    Summary

    This study introduces a novel method using unlabeled video data for few-shot video classification. It enhances classification accuracy by employing a label-independent memory and a multi-modality network, effectively utilizing vast unlabeled datasets.

    Related Experiment Videos

    Last Updated: Dec 13, 2025

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.4K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Few-shot video classification presents challenges due to limited labeled data.
    • Leveraging large unlabeled video datasets is crucial for improving performance.
    • Existing methods struggle with imbalanced and dynamic video data.

    Purpose of the Study:

    • To develop a semi-supervised approach for few-shot video classification using unlabeled data.
    • To enhance classification robustness and accuracy in low-data regimes.
    • To effectively utilize both visual and motion dynamics from video data.

    Main Methods:

    • Proposed a label-independent memory (LIM) to generate robust class prototypes from unlabeled data.
    • Integrated a multi-modality compound memory network capturing both RGB and flow information.
    • Employed joint optimization of separate RGB and flow memory networks via a unified loss function.

    Main Results:

    • The LIM effectively caches label-related features for similarity search.
    • The multi-modality network leverages cross-modal communication for improved classification.
    • Experiments on Kinetics-100 and Something-Something-100 datasets validate the approach's effectiveness.

    Conclusions:

    • Freely available unlabeled video data can significantly facilitate few-shot video classification.
    • The proposed LIM and multi-modality network offer a robust solution for semi-supervised few-shot learning.
    • This work demonstrates a promising direction for utilizing large-scale unlabeled video resources.