AIBullisharXiv – CS AI · 7h ago7/10
🧠
Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks
Researchers demonstrate that parameter-efficient fine-tuning (PEFT) methods like adapters and LoRA can achieve competitive performance on instance segmentation tasks while training only 1-6% of model parameters, compared to 40-55% in traditional fine-tuning. The findings highlight that context-specific optimization is crucial, with 2-3 adapters per transformer block providing optimal efficiency gains.