GeoBias: Geometric Bias Correction via Decision Boundary Translation for Long-Tailed Recognition

Real-world visual recognition tasks often exhibit long-tailed label distributions, where a few head classes dominate training while most tail classes are underrepresented. Standard empirical risk minimization biases classifiers toward head classes, severely degrading tail-class generalization. Existing decoupling methods mainly adjust classifier weights, overlooking the bias term as an independent lever for decision-boundary correction. We propose GeoBias, a geometry-driven bias correction method that fixes the learned weight directions and analytically computes bias shifts from class centroids and pairwise boundary geometry. Multi-class conflicts are resolved by weighted aggregation, and a time-decaying anchoring regularizer prevents SGD from eroding the corrected biases. GeoBias is single-stage and plug-and-play, requiring no data re-sampling, architectural modification, or complex training schedule. Experiments on CIFAR-10-LT, CIFAR-100-LT, and ImageNet-LT show that GeoBias consistently improves tail-class accuracy and achieves competitive or superior performance relative to single-stage and two-stage methods. Code will be released upon acceptance.