# Class-Adaptive Sparse Activation (CASA) Classifier

Long-tailed recognition no longer treats every class alike. Prior methods pulled all features onto one fixed equiangular tight frame, a shared target whose directions make equal angles, and that full collapse over-contracts tail samples and weakens generalization. Head classes still collapse onto the frame while tail classes keep within-class diversity: a feature-conditioned gating classifier specializes nodes so tail classes take their own directions, and a frequency-adaptive margin shrinks for head classes and grows for tail classes. On CIFAR100-LT, ImageNet-LT, Places-LT and iNaturalist-2018, overall accuracy beats prior methods, and within-class spread rises monotonically from head to tail.

Long-tailed recognition no longer treats every class alike. Prior methods pulled all features onto one fixed equiangular tight frame, a shared target whose directions make equal angles, and that full collapse over-contracts tail samples and weakens generalization. Head classes still collapse onto the frame while tail classes keep within-class diversity: a feature-conditioned gating classifier specializes nodes so tail classes take their own directions, and a frequency-adaptive margin shrinks for head classes and grows for tail classes. On CIFAR100-LT, ImageNet-LT, Places-LT and iNaturalist-2018, overall accuracy beats prior methods, and within-class spread rises monotonically from head to tail.