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Related Experiment Video

Updated: Aug 4, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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Semantic and Relation Modulation for Audio-Visual Event Localization.

Hao Wang, Zheng-Jun Zha, Liang Li

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 4, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a Semantic and Relation Modulation Network (SRMN) for precise audio-visual event localization. The SRMN effectively captures correlations between audio and visual data segments to improve event detection accuracy.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Signal Processing

    Background:

    • Current audio-visual event localization methods often overlook crucial correlations between audio and visual segments.
    • Existing approaches fail to fully leverage multi-scale event proposals and dense cross-modal segment matching.

    Purpose of the Study:

    • To develop a novel Semantic and Relation Modulation Network (SRMN) for enhanced audio-visual event localization.
    • To effectively learn and utilize correlations between audio and visual modalities and across multi-scale event proposals.

    Main Methods:

    • Proposed a Semantic and Relation Modulation Network (SRMN) incorporating intra-modal and cross-modal normalization for semantic modulation.
    • Introduced multi-scale proposal and multi-alignment segment modulating modules for relation modulation.
    • Leveraged modulated features for accurate audio-visual event localization.

    Main Results:

    • The SRMN effectively captures and utilizes correlations between audio-visual segments and multi-scale event proposals.
    • Achieved state-of-the-art performance in supervised event localization on the AVE dataset.
    • Demonstrated superior results in cross-modality localization tasks.

    Conclusions:

    • The proposed SRMN significantly improves audio-visual event localization by effectively modeling inter-modal and multi-scale correlations.
    • The method offers a robust framework for accurate detection of audible and visible events in videos.
    • SRMN outperforms existing methods, establishing a new benchmark for audio-visual event localization.