CAFE: Closed-Loop Classifier-Aware Feature Enrichment for Long-Tailed Recognition

Long-tailed recognition remains challenging due to classifier bias induced by highly imbalanced training data. Existing information augmentation methods often overlook which tail samples actually require augmentation and whether generated features remain beneficial as the classifier evolves. To address these issues, we propose Classifier-Aware Feature Enrichment (CAFE), a two-stage classifier fine-tuning framework that establishes a closed-loop augmentation process. Guided by the evolving classifier, CAFE identifies challenging tail samples and selectively augments them using informative features transferred from data-rich classes. The generated features are continuously evaluated, and beneficial synthetic features are retained while redundant or detrimental ones are removed. The updated classifier subsequently guides the next round of sample identification and feature enrichment, allowing the augmentation process to adapt continuously to the evolving classifier. Extensive experiments on CIFAR10-LT, CIFAR100-LT, ImageNet-LT, and iNaturalist 2018 demonstrate the effectiveness of CAFE and show that it achieves competitive performance against state-of-the-art methods. Code will be made publicly available.