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  3. Active Learning For Object Detection With Vectorized Dual Pseudo Loss And Multiple Instance Offset Constraint.
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  3. Active Learning For Object Detection With Vectorized Dual Pseudo Loss And Multiple Instance Offset Constraint.

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Active Learning for Object Detection With Vectorized Dual Pseudo Loss and Multiple Instance Offset Constraint.

Jiachen Yang, Jiasai Wu, Shuai Xiao

    IEEE Transactions on Cybernetics
    |July 22, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a novel active learning method for object detection, utilizing vectorized dual pseudo loss and instance offset constraints to improve unlabeled data selection. The approach enhances information quality evaluation and diversity-driven sampling, outperforming existing methods.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Computer Vision

    Background:

    • Active learning methods for object detection struggle with missing ground truth labels for regression loss.
    • Challenges include poor representation of unlabeled instance information and quality discrepancies between image and anchor levels.

    Purpose of the Study:

    • To propose an advanced active learning method for object detection.
    • To address limitations in existing methods regarding regression loss, unlabeled data representation, and information quality.

    Main Methods:

    • A two-stage framework is introduced: image information quality evaluation and diversity-driven sampling.
    • Pioneers a dual pseudo loss formulation for theoretically grounded regression loss estimation.
    • Employs instance-level cosine similarity for effective removal of redundant images.

    Main Results:

    • The proposed method significantly outperforms state-of-the-art active learning approaches on PASCAL VOC and MS COCO datasets.
    • The dual pseudo regression loss effectively captures regression information quality.
    • Demonstrates robust performance in active learning for object detection tasks.

    Conclusions:

    • The novel active learning method enhances object detection by improving data selection strategies.
    • The dual pseudo loss formulation is a key innovation for robust regression information quality assessment.
    • The method offers a significant advancement for efficient and effective object detection model training.