Empirical tests show adapters (2-3 per block) and LoRA on deformable attention achieve competitive instance segmentation with 1-6% parameters tuned versus 40-55% for full fine-tuning.
Learning to Adapt Foundation Model DINOv2 for Capsule Endoscopy Diagnosis.arXiv2024, arxiv:2406.10508
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Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks
Empirical tests show adapters (2-3 per block) and LoRA on deformable attention achieve competitive instance segmentation with 1-6% parameters tuned versus 40-55% for full fine-tuning.