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Adaptive RGB Image Recognition by Visual-Depth Embedding.

Ziyun Cai, Yang Long, Ling Shao

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    This study introduces adaptive Visual-Depth Embedding (aVDE) to improve RGB image recognition using RGB-D data. The method effectively addresses domain shift and leverages depth information for enhanced object and scene classification.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Data Science

    Background:

    • Recognizing RGB images from RGB-D data offers cost reduction with high accuracy.
    • Existing methods face domain shifting issues due to differing camera and sensor mechanisms.
    • Challenges include utilizing depth information and minimizing domain distribution mismatch.

    Purpose of the Study:

    • To develop a method for recognizing RGB images using RGB-D data.
    • To address the domain shifting problem in cross-modal recognition.
    • To effectively leverage depth information for improved recognition rates.

    Main Methods:

    • Proposed adaptive Visual-Depth Embedding (aVDE) to learn a shared latent space.
    • Transferred depth information from labeled source domain to unlabeled target dataset.
    • Unified optimization problem combining feature matching and instance reweighting for domain adaptation.

    Main Results:

    • Tested on five dataset pairs for object recognition and scene classification.
    • Demonstrated effectiveness in improving recognition rates despite domain shift.
    • Successfully transferred knowledge from RGB and depth modalities.

    Conclusions:

    • aVDE effectively utilizes depth information for RGB image recognition.
    • The method significantly reduces the domain distribution mismatch.
    • aVDE provides an adaptive classifier for improved cross-domain recognition performance.