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cs.LG2026
Balancing Multimodal Learning via Functional Progress
Zhongjing Gu, Fengqiang Wan, Yiming Cui +2
Multimodal learning often suffers from modality imbalance, where the joint optimization process is dominated by a single modality. Existing methods typically estimate modality imba…
cs.LG2026
SR-LoRA: Self-Rectifying Inter-layer Relations in Low-Rank Adaptation for Class-Incremental Learning
Fengqiang Wan, Yipeng Lin, Kan Lv +1
Pre-trained models with parameter-efficient fine-tuning (PEFT) have demonstrated promising potential for class-incremental learning (CIL), yet catastrophic forgetting still persist…
cs.LG2026
AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels
Fengqiang Wan, Qing-Yuan Jiang, Fu Shen +1
Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training…