Ontology-Guided Semantic Mixing and Adaptive Low-Rank Experts for Long-Tailed Recognition

Learning from long-tailed data remains an obstacle for recognition systems, because rare categories carry the greatest decision value but provide the weakest training signal. However, correcting class imbalance alone does not ensure that augmented samples preserve domain-specific semantics. Mixing-based augmentation guided by generic vision-language embeddings selects partners by linguistic co-occurrence rather than domain semantics, injecting semantic noise where data are scarcest. We propose a knowledge-guided learning framework that couples an explicit symbolic knowledge source with parameter-efficient adaptation. Ontology-Guided Semantic Mixing (OGSM) transforms frozen CLIP text embeddings into ontology-consistent representations through triplet metric learning over expertcurated relations, producing a similarity matrix that restricts augmentation to semantically admissible class pairs. Bag of LoRAs (BAGL) then supplies the discriminative capacity the enriched distribution demands. The number of low-rank experts is derived in closed form from the class count and the imbalance factor, which removes per-dataset hyperparameter search, and per-expert logit tempering is optimized by a secondary objective that jointly minimizes mean task loss and inter-expert loss variance. Because the knowledge component is precomputed and architecture-agnostic, it transfers across tasks without modification. On four long-tailed land-cover benchmarks the framework attains 91.1% average accuracy with consistent tail-class gains over the strongest baseline, and OGSM alone improves tail mIoU on iSAID segmentation by 14.5 points over CutMix.