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Imbalance Problems in Object Detection: A Review.

Kemal Oksuz, Baris Can Cam, Sinan Kalkan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 20, 2020
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    Summary
    This summary is machine-generated.

    This paper reviews object detection imbalance problems using a new taxonomy. It offers insights into existing solutions and identifies new research challenges for object detection algorithms.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Object detection models frequently encounter data imbalance issues, where certain object classes are underrepresented.
    • This imbalance significantly degrades model performance, particularly for minority classes.

    Purpose of the Study:

    • To systematically review and categorize the multifaceted imbalance problems in object detection.
    • To provide a critical perspective on current solutions and identify underexplored research areas.

    Main Methods:

    • Introduction of a novel problem-based taxonomy for classifying imbalance issues.
    • In-depth analysis of each problem category and existing literature solutions.
    • Creation of an accompanying webpage to catalog relevant research papers.

    Main Results:

    • A structured taxonomy that categorizes object detection imbalance problems.
    • A critical review of existing solutions, highlighting their strengths and limitations.
    • Identification of both existing open issues and novel imbalance problems.

    Conclusions:

    • The proposed taxonomy offers a systematic framework for understanding and addressing object detection imbalance.
    • Further research is needed to tackle the identified open and novel imbalance problems.
    • The accompanying webpage serves as a dynamic resource for the research community.