Real-world datasets are inherently class-imbalanced, leading models to favor majority classes and struggle with minority ones. Existing logit adjustment methods address this by modifying logits based on class frequencies, but their class-wise adjustments ignore instance-wise confusion and often suppress informative signals from confusing non-target classes.

This results in insufficient learning of such classes, increased prediction uncertainty, and limited generalization. To address this, we propose two complementary methods.

First, Confusion-based Instance-wise Logit Adjustment (CILA) adaptively adjusts logits based on instance-wise confusion between the target and non-target classes, restoring informative gradients that class-wise adjustment suppresses. Second, Batch-adaptation Noise-Injection (BNI) perturbs the target logit during training, extending the confusion-aware update to correctly classified but near-boundary samples that the penalty alone cannot reach. CILA is a loss-agnostic plug-in module that can be seamlessly integrated into existing long-tailed learning methods, and consistently improves performance across multiple benchmarks.

Our code will be available at https://github.com/maya0395/CILA.