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    This study introduces Label-aware Calibration and Relation-preserving (LabCR) to improve visual intention understanding by addressing label shifting and label blemish in social media images. LabCR enhances accuracy by calibrating intentions and preserving sample relationships.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Visual intention understanding in social media is challenging due to implicit semantics.
    • Ambiguous definitions lead to label shifting (intention discrepancies under augmentation) and label blemish (annotation errors).

    Purpose of the Study:

    • To propose a novel method, Label-aware Calibration and Relation-preserving (LabCR), to address label shifting and label blemish.
    • To improve the accuracy and robustness of visual intention understanding models.

    Main Methods:

    • LabCR disentangles multiple intentions for explicit distribution calibration, addressing label shifting via consistent inferred intentions in augmented instance pairs.
    • LabCR utilizes intention similarity to establish correlations among samples, providing supervision signals for correlation alignments to alleviate label blemish.

    Main Results:

    • Extensive experiments validate the superiority of the proposed LabCR method.
    • The method demonstrates effectiveness in visual intention understanding and pedestrian attribute recognition tasks.

    Conclusions:

    • LabCR effectively alleviates label shifting and label blemish in visual intention understanding.
    • The proposed method offers a significant advancement in accurately interpreting implicit semantics from images.