Adaptive ambiguity-aware weighting for multi-label recognition with limited annotations.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Multi-label recognition faces challenges with partial labels, increasing annotation costs and limiting model generalization.
• Current methods often use unrealistic label dropping simulations, failing to account for real-world instance ambiguity.

Purpose of the Study:

• To propose a realistic partial label setting for multi-label recognition based on instance ambiguity.
• To introduce a novel strategy, Reliable Ambiguity-Aware Instance Weighting (R-AAIW), for dynamic instance weighting.

Main Methods:

• Developed a realistic partial label setting that considers instance ambiguity.
• Implemented R-AAIW, a strategy using importance weighting and an ambiguity score to prioritize learning.
• Employed adaptive re-weighting to dynamically adjust focus from clearer to more ambiguous instances as model proficiency increases.

Main Results:

• The proposed R-AAIW strategy effectively addresses limitations of existing methods in handling partial labels.
• Experiments demonstrate superior performance across various benchmarks compared to current approaches.
• The method enhances the detection of subtle label variations and ensures comprehensive learning.

Conclusions:

• The R-AAIW approach provides a more accurate and adaptable framework for multi-label recognition.
• This strategy effectively reduces annotation costs and improves model generalization in realistic partial label scenarios.
• The dynamic weighting mechanism successfully handles instance-level ambiguity, improving overall recognition performance.