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Competing for Pixels: A Self-Play Algorithm for Weakly-Supervised Semantic Segmentation.

Shaheer U Saeed, Shiqi Huang, Joao Ramalhinho

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 3, 2024
    PubMed
    Summary
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    This study introduces a novel weakly-supervised semantic segmentation (WSSS) method using reinforcement learning (RL) self-play to gamify image segmentation. The approach trains agents to compete for object patches, significantly improving segmentation accuracy and reducing common WSSS errors.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Weakly-supervised semantic segmentation (WSSS) relies on image-level labels, lacking precise region correspondence.
    • WSSS offers reduced annotation costs compared to fully-supervised methods.
    • Existing WSSS methods struggle with over-segmentation and under-segmentation.

    Purpose of the Study:

    • To propose a novel WSSS method using reinforcement learning (RL) self-play.
    • To gamify the image segmentation process for regions of interest (ROIs).
    • To address the challenge of accurate ROI localization with limited supervision.

    Main Methods:

    • Formulated segmentation as a competitive game between two RL agents.
    • Agents select ROI-containing patches until all such patches are exhausted.

    Related Experiment Videos

  • Utilized an object presence detector pre-trained on image-level binary labels.
  • Implemented a game termination condition and agent incentives based on patch selection success.

    • Main Results:

    • Demonstrated significant performance improvements across four diverse datasets.
    • Outperformed recent state-of-the-art WSSS methods.
    • Effectively minimized over- and under-segmentation issues inherent in WSSS.

    Conclusions:

    • The proposed RL self-play gamified segmentation approach is effective for WSSS.
    • This method offers a robust solution for accurate ROI segmentation with weak supervision.
    • The competitive agent framework successfully mitigates common WSSS segmentation errors.