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

In multi-label recognition, effectively addressing the challenge of partial labels is crucial for reducing annotation costs and enhancing model generalization. Existing methods exhibit limitations by relying on unrealistic simulations with uniformly dropped labels, overlooking how ambiguous instances and instance-level factors impacts label ambiguity in real-world datasets. To address this deficiency, our paper introduces a realistic partial label setting grounded in instance ambiguity, complemented by Reliable Ambiguity-Aware Instance Weighting (R-AAIW)-a strategy that utilizes importance weighting to adapt dynamically to the inherent ambiguity of multi-label instances.