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Fine-Grained Image Analysis With Deep Learning: A Survey.

Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 9, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Fine-grained image analysis (FGIA) is a challenging computer vision task. This survey details deep learning advances in FGIA, consolidating recognition and retrieval, and highlighting future research directions.

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    Area of Science:

    • Computer Vision and Pattern Recognition

    Background:

    • Fine-grained image analysis (FGIA) addresses challenges in distinguishing subordinate categories (e.g., bird species, car models).
    • Small inter-class and large intra-class variations make FGIA a difficult problem.

    Purpose of the Study:

    • To present a systematic survey of recent advances in deep learning-powered FGIA.
    • To redefine and broaden the FGIA field by integrating image recognition and retrieval.
    • To review benchmark datasets and domain-specific applications.

    Main Methods:

    • Systematic literature review of deep learning approaches in FGIA.
    • Consolidation of fine-grained image recognition and fine-grained image retrieval research.
    • Analysis of publicly available FGIA benchmark datasets.

    Main Results:

    • Significant progress in deep learning-based FGIA methods has been observed.
    • The survey consolidates FGIA into recognition and retrieval, broadening its scope.
    • Key datasets and applications are reviewed.

    Conclusions:

    • FGIA is a critical area in computer vision with diverse applications.
    • Further research is needed in several identified directions and open problems.
    • Deep learning has substantially advanced FGIA capabilities.