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Enabling Visual Object Detection With Object Sounds via Visual Modality Recalling Memory.

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    This study introduces a novel sound-based object detector that mimics human auditory-visual integration. The system uses visual modality recalling (VMR) memory to detect objects using sound alone, advancing conventional visual-only methods.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Conventional object detection relies heavily on visual data.
    • Human object recognition integrates auditory and visual information.
    • A gap exists in sound-based object detection systems.

    Purpose of the Study:

    • To develop a novel sound-based object detector.
    • To mimic human auditory-visual integration for object detection.
    • To enable object detection using only sound input.

    Main Methods:

    • Designed a visual modality recalling (VMR) memory to retrieve visual information from audio input.
    • Proposed VMR loss and audio-visual association loss to train the VMR memory.
    • Implemented audio-visual integration using recalled visual information and original audio.
    • Introduced integrated feature contrastive loss for enhanced feature embedding.

    Main Results:

    • The proposed sound-based object detector effectively performs object detection using only sound.
    • Experimental results validate the effectiveness of the VMR memory and proposed losses.
    • The method demonstrates a new perspective beyond traditional visual-only object detection.

    Conclusions:

    • The novel sound-based object detector successfully leverages auditory information for object recognition.
    • The VMR memory and associated losses are crucial for recalling and integrating cross-modal information.
    • This research offers a significant advancement in object detection by incorporating sound as a primary input.