CILA: Confusion-based Instance-wise Logit Adjustment for Long-Tailed Recognition
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, \textbf{Confusion-based Instance-wise Logit Adjustment (CILA)} adaptively adjusts logits based on instance-wise confusion between the target and non-target cl...
| Authors | Jiyoung Lim, Jee-Hyong Lee |
|---|---|
| Primary category | cs.CV |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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