TailCon: Mitigating Tail Signal Erosion through Memory Consolidation for Long-Tailed Recognition

Long-tailed recognition remains challenging not only because rare classes provide limited supervision, but also because their evidence is difficult to accumulate during standard stochastic training. We characterize this failure as tail signal erosion: in conflict-dominant regimes, frequent-class updates can dilute sparse rare-class signals before they are sufficiently reinforced. To address this issue, we propose TailCon, a memory consolidation framework that separates rare-evidence acquisition from parametric integration. During online acquisition, TailCon stores uncertain or rare representations in detached episodic memory, while a parametric prediction pathway learns global decision structure. To avoid relying only on test-time retrieval, TailCon introduces FOCUS, a periodic consolidation stage that distills stored memory evidence into the parametric prediction head through a low-capacity alignment surrogate. We provide a local optimization analysis that scopes when this separation is beneficial in sparse-recurrence, conflict-dominant regimes. Experiments on CIFAR-100-LT, CIFAR-10-LT, ImageNet-LT, and iNaturalist 2018 show consistent improvements over representative rebalancing, decoupled, expert-based, and prototype-oriented baselines under the evaluated protocols. Memory-disabled inference further shows that part of the stored evidence is transferred into the parametric prediction head rather than remaining only in the retrieval branch. An anonymized implementation is included in the Supplementary Material.