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Branches Mutual Promotion for End-to-End Weakly Supervised Semantic Segmentation.

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    This study introduces a novel approach for end-to-end weakly supervised semantic segmentation (E2E-WSSS) by enabling equal contribution and mutual promotion between classification and segmentation branches, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • End-to-end weakly supervised semantic segmentation (E2E-WSSS) trains models using only image-level annotations.
    • Current E2E-WSSS methods often have a classification branch dominate the training, limiting mutual assistance between branches.

    Purpose of the Study:

    • To develop a novel E2E-WSSS method that treats classification and segmentation branches equally.
    • To enhance mutual promotion between the two branches through bidirectional supervision and interaction operations.

    Main Methods:

    • Proposed a bidirectional supervision mechanism to enforce consistency between classification and segmentation outputs.
    • Introduced interaction operations for knowledge exchange between the two branches.
    • Treated both branches as diverse methods for generating segmentation maps.

    Main Results:

    • The proposed method achieved superior performance compared to existing E2E-WSSS approaches.
    • Demonstrated effective mutual promotion between the classification and segmentation branches.
    • Showcased enhanced quality of localization seeds through feedback from the segmentation branch.

    Conclusions:

    • The novel approach effectively promotes collaboration between classification and segmentation branches in E2E-WSSS.
    • Equal treatment and interaction lead to improved segmentation performance.
    • This work offers a new direction for optimizing E2E-WSSS models.