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cs.CV2025
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy
Yiting Yang, Hao Luo, Yuan Sun +7
A prevalent approach in Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViT) involves freezing the majority of the backbone parameters and solely learnin…
cs.CV2024
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation
Wei Dong, Yuan Sun, Yiting Yang +7
A common strategy for Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks by learning a low-rank adapta…
cs.CV2024
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach
Wei Dong, Xing Zhang, Bihui Chen +5
Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while…