U-MOF: Uncertainty-Guided Parameter-Efficient Multi-Objective Fine-Tuning for Long-Tailed Recognition

Recent long-tailed recognition methods increasingly adopt two-stage fine-tuning, where a generic representation is first learned and imbalance-aware adaptation is then performed in a lightweight second stage. However, existing second-stage designs often allocate objective emphasis using delayed empirical summaries, such as historical class-wise performance or validation feedback, and commonly restrict adaptation to classifier heads. These choices may make routing unreliable when empirical feedback is noisy or skewed, and may also limit the feature plasticity required for rare categories. In this paper, we propose U-MOF, an uncertainty-guided parameter-efficient multi-objective fine-tuning framework for long-tailed recognition. U-MOF introduces lightweight residual adapters into a shared-backbone expert committee, uses entropy-based committee statistics as diagnostic routing proxies to modulate tail-oriented and smoothing-oriented objectives, and applies conflict-aware orthogonal projection to coordinate heterogeneous objective gradients. Experiments on CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018 show that U-MOF improves rare-category and overall performance in controlled stage-two comparisons and remains competitive with strong published long-tailed recognition baselines, while preserving the efficiency advantages of decoupled adaptation. These results indicate that reliable stage-two long-tailed fine-tuning benefits not only from selecting appropriate objectives, but also from diagnosing when each objective should dominate and from retaining limited feature-level plasticity. An anonymized implementation is included in the Supplementary Material.