The arXiv preprint "CILA: Confusion-based Instance-wise Logit Adjustment for Long-Tailed Recognition" addresses the common problem of class imbalance in real-world datasets. The abstract notes that such imbalance leads models to favor majority classes and struggle with minority ones, a central challenge in long-tailed recognition.
Existing logit adjustment methods, the abstract explains, modify logits based on class frequencies. The new approach, CILA, is described in the title as confusion-based and instance-wise, suggesting it may tailor adjustments to individual examples using confusion information rather than relying solely on frequency counts.
The available abstract is truncated, so the full mechanism and experimental results are not included in the source. This is a single arXiv announcement, so no independent comparisons or confirmations are available.