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Related Experiment Video

Updated: Jun 28, 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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Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition.

Yukun Zuo, Hantao Yao, Liansheng Zhuang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 12, 2024
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    Summary

    This study introduces Hierarchical Augmentation and Distillation (HAD) to solve catastrophic forgetting in class incremental audio-visual video recognition. HAD effectively preserves historical knowledge by leveraging hierarchical data and model structures.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Audio-visual video recognition (AVVR) integrates audio and visual data for accurate video categorization.
    • Current AVVR methods struggle with catastrophic forgetting when encountering new classes, lacking methods for class incremental learning.
    • Existing incremental learning approaches overlook the inherent hierarchical structures in audio-visual data and models.

    Purpose of the Study:

    • To address the challenge of catastrophic forgetting in audio-visual video recognition under class incremental learning scenarios.
    • To propose a novel method, Class Incremental Audio-Visual Video Recognition (CIAVVR), that preserves historical knowledge.
    • To fully exploit the hierarchical structures present in both data and models for effective knowledge preservation.

    Main Methods:

    • Introduced Hierarchical Augmentation and Distillation (HAD), comprising Hierarchical Augmentation Module (HAM) and Hierarchical Distillation Module (HDM).
    • HAM utilizes segmental feature augmentation to preserve hierarchical model knowledge.
    • HDM employs hierarchical logical distillation (video-distribution) and hierarchical correlative distillation (snippet-video) for intra-sample and inter-sample knowledge preservation.

    Main Results:

    • Evaluations on AVE, AVK-100, AVK-200, and AVK-400 benchmarks demonstrated HAD's effectiveness.
    • HAD successfully captures hierarchical information, significantly enhancing the preservation of historical class knowledge.
    • The proposed method shows improved performance in class incremental audio-visual video recognition tasks.

    Conclusions:

    • HAD offers an effective solution for catastrophic forgetting in class incremental audio-visual video recognition.
    • Exploiting hierarchical structures in data and models is crucial for preserving knowledge in incremental learning.
    • The theoretical analysis supports the efficacy of the segmental feature augmentation strategy.