Uwe Handmann
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126
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2026
Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks
This paper investigates the application of Parameter-Efficient Fine-Tuning (PEFT) methods, specifically adapters and LoRA, to large pretrained models for instance segmentation, demonstrating that these techniques achieve competitive performance while drastically reducing the number of trainable parameters.
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