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Negative Deterministic Information-Based Multiple Instance Learning for Weakly Supervised Object Detection and

Guanchun Wang, Xiangrong Zhang, Zelin Peng

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    |May 15, 2024
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    Summary
    This summary is machine-generated.

    This study introduces a novel Multiple Instance Learning (MIL) method using negative deterministic information (NDI) to improve weakly supervised object detection and semantic segmentation. The approach effectively addresses issues like discriminative instance domination and missing instances, enhancing localization accuracy.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Weakly supervised object detection (WSOD) and semantic segmentation leverage image-level annotations for label efficiency.
    • Multiple Instance Learning (MIL) is a common approach, treating images as bags of instances.

    Purpose of the Study:

    • To address limitations in conventional MIL, such as discriminative instance domination and missing instances.
    • To propose a novel MIL paradigm, NDI-MIL, utilizing negative deterministic information (NDI).

    Main Methods:

    • NDI-MIL employs two core designs: NDI collection and negative contrastive learning (NCL).
    • NDI is collected from negative instances via a dynamic feature bank.
    • NCL uses NDI to penalize discriminative regions, mitigating instance domination and omissions.

    Main Results:

    • The proposed method effectively addresses discriminative instance domination and missing instances.
    • NDI-MIL demonstrates improved object- and pixel-level localization accuracy and completeness.
    • An NDI-guided instance selection (NGIS) strategy further enhances performance.

    Conclusions:

    • NDI-MIL offers a robust solution for WSOD and semantic segmentation tasks.
    • The method shows satisfactory performance on benchmarks like PASCAL VOC and MS COCO.