Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks
Research and applications in artificial intelligence have recently shifted with the rise of large pretrained models, which deliver state-of-the-art results across numerous tasks. However, the substantial increase in parameters introduces a need for parameter-efficient training strategies. Despite significant advancements, limited research has explored parameter-efficient fine-tuning (PEFT) methods in the context of transformer-based models for instance segmentation. Addressing this gap, this study investigates the effectiveness of PEFT methods, specifically adapters and Low-Rank Adaptation (LoRA), applied to two models across four benchmark datasets. Integrating sequentially arranged adap...
| Authors | Nermeen Abou Baker, David Rohrschneider, Uwe Handmann |
|---|---|
| Primary category | cs.CV |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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