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Updated: Sep 19, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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Prompt-guided consistency learning for multi-label classification with incomplete labels.

Shouwen Wang1, Qian Wan2, Zihan Zhang1

  • 1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China; Key Lab of Image Processing and Intelligent Control, Ministry of Education, Wuhan, 430074, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 5, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a prompt-guided consistency learning (PGCL) framework to improve multi-label classification with incomplete data. The method reduces confirmation bias and visual confusion, achieving state-of-the-art results in settings with partial and single positive labels.

Keywords:
Confirmation biasContrastive learningIncomplete labelsMulti-label classificationPseudo-labelingSemantic decoupling

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Multi-label classification with incomplete annotations faces challenges with insufficient supervision and model generalization.
  • Pseudo-labels can introduce noise and confirmation bias, while self-correction methods struggle with visual confusion.

Purpose of the Study:

  • To propose a novel prompt-guided consistency learning (PGCL) framework to address confirmation bias and visual confusion in multi-label classification.
  • To enhance model generalization and performance in settings with partial and single positive labels.

Main Methods:

  • Introduced an intra-category supervised contrastive loss for consistency constraints within each category's feature space.
  • Developed a class-specific semantic decoupling module leveraging CLIP for improved label-level representations.
  • Implemented label-level contrasting to differentiate true positives from visually confusing samples.

Main Results:

  • The PGCL framework effectively reduces confirmation bias and mitigates visual confusion.
  • Demonstrated state-of-the-art performance on multiple datasets for the targeted incomplete labeling settings.
  • Showcased the benefits of intra-category contrastive loss and class-specific semantic decoupling.

Conclusions:

  • The proposed PGCL framework offers a robust solution for multi-label classification with incomplete annotations.
  • The method successfully improves model generalization and tackles challenges posed by noisy pseudo-labels and visual confusion.
  • Achieved superior performance, highlighting the effectiveness of the novel contrastive loss and semantic decoupling strategies.