Abstract Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative
for full fine-tuning so as to adapt pre-trained vision models to downstream tasks, which only …

Large-scale pre-trained vision models (PVMs) have shown great potential for adaptability
across various downstream vision tasks. However, with state-of-the-art PVMs growing to …

Pre-trained vision transformers have strong representations benefit to various downstream
tasks. Recently many parameter-efficient fine-tuning (PEFT) methods have been proposed …

Robust fine-tuning aims to achieve competitive in-distribution (ID) performance while
maintaining the out-of-distribution (OOD) robustness of a pre-trained model when …

Fine-tuning a visual pre-trained model can leverage the semantic information from large-
scale pre-training data and mitigate the over-fitting problem on downstream vision tasks with …

Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-
tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers …

Parameter-efficient transfer learning (PETL) is an emerging research spot aimed at
inexpensively adapting large-scale pre-trained models to downstream tasks. Recent …

Federated Learning (FL) is an emerging paradigm that enables distributed users to
collaboratively and iteratively train machine learning models without sharing their private …

Among the widely used parameter-efficient finetuning (PEFT) methods, LoRA and its
variants have gained considerable popularity because of avoiding additional inference …

Current state-of-the-art results in computer vision depend in part on fine-tuning large pre-
trained vision models. However, with the exponential growth of model sizes, the …