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Updated: Jul 9, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond.

Yue Yao, Liang Zheng, Xiaodong Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 1, 2023
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    Summary
    This summary is machine-generated.

    This study introduces an attribute descent approach to bridge the content-level domain gap in synthetic data, making it more realistic for computer vision tasks. Optimized synthetic data improves performance in image classification and object re-identification.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Computer Graphics

    Background:

    • A significant domain gap exists between synthetic and real-world data in computer vision.
    • This gap has two levels: appearance (style) and content (attributes like viewpoint, lighting).
    • The content-level domain gap is less studied but crucial for realistic data simulation.

    Purpose of the Study:

    • To address the content-level domain gap in synthetic data generation.
    • To propose and validate an automated method for optimizing synthetic data attributes.
    • To enhance the utility of synthetic data for various computer vision applications.

    Main Methods:

    • An attribute descent approach is proposed to automatically optimize engine attributes.
    • The method focuses on object-centric tasks where optimization signals are clear.
    • New synthetic assets (VehicleX) were collected, and existing ones (ObjectX, PersonX) were reused.

    Main Results:

    • The attribute descent approach effectively reduces the content-level domain gap.
    • Experiments on image classification and object re-identification show improved performance.
    • Adapted synthetic data proved effective in training-only, data augmentation, and dataset understanding scenarios.

    Conclusions:

    • Optimizing synthetic data attributes is key to bridging the content-level domain gap.
    • The proposed method enables the effective use of synthetic data in real-world computer vision tasks.
    • This approach offers a viable solution for generating high-quality, realistic training data.