Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model

arXiv:2311.17112v2 Announce Type: replace
Abstract: Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data. In the computer vision community, PEFT has shown effectiveness in image classification, but little research has studied its ability for image segmentation. Fine-tuning segmentation models usually require a heavier adjustment of parameters to align the proper projection directions in the parameter space for new scenarios. This raises a challenge to existing PEFT algorithms, …

abstract arxiv block classification community computer computer vision cs.cv data fine-tuning foundation image methodology novel orchestration peft research segment segment anything segment anything model segmentation training training data type via vision