DualTrack: Accessible Knowledge Transfer for Continual Learning under Long-Tailed Distributions

Long-tailed continual learning requires models to acquire new classes from imbalanced task streams while retaining previously observed classes without replaying historical images. The challenge extends beyond catastrophic forgetting: few-shot classes provide unreliable representation estimates, and added experts can make historical knowledge inaccessible even when their parameters remain unchanged. We present DualTrack, a framework that couples stable coordinates, statistical routing, protected adaptation, and tail-class transfer. A frozen ViT-B/16 backbone is augmented with early shared orthogonal LoRA modules and late general/specific LoRA branches. An independent routing stream maintains class-conditional diagonal Gaussian statistics to decide whether a new class should reuse an existing expert or receive additional capacity. When an expert absorbs a class, rank expansion preserves its initial function, freezes the old rank prefix, and trains only newly allocated directions; historical gradient-direction projection further restricts updates to directions compatible with prior adaptation. Finally, image-free class prototypes are transferred to tail classes using visual similarity, cross-layer route overlap, and a count-adaptive shrinkage coefficient. We evaluate on long-tailed continual-learning benchmarks and compare DualTrack with replay, regularization, prompt, expandable-expert, and prototype-based baselines under matched ViT backbones.