MemTailor: Hierarchical Memory-Augmented Multi-Expert Learning for Long-Tailed Recognition

Long-tailed visual recognition is limited not only by biased optimization, but also by the under-retention of rare-class evidence in parameters learned from imbalanced data. MemTailor addresses this limitation by augmenting a frequency-aware multi-expert backbone with a hierarchical class-aware memory that preserves visual evidence outside the backbone. The memory stores per-class prototypes and bounded instance slots for each expert, allocates proportionally richer capacity to rare classes, and records both shallow and deep features to retain complementary discriminative cues. During training, same-class memory retrieval constructs class-adaptive feature augmentation and provides an auxiliary supervised objective on the augmented features. At inference, class-level memory quality and prediction uncertainty determine when stored evidence is allowed to correct expert logits, and the final prediction fuses memory-corrected logits across multiple input scales. Experiments on long-tailed visual recognition benchmarks show that MemTailor improves balanced recognition, especially for under-represented classes.