The current research in parameter-efficient fine-tuning (PEFT) of vision transformers is advancing towards more nuanced and context-aware adaptation strategies. Innovations are focusing on enhancing the model's ability to capture high-frequency features and complex data interactions, which are crucial for distinguishing subtle image structures and improving accuracy across various tasks. Techniques are being developed to integrate physical priors and singular value decomposition to better balance generalizability and task-specific learning, leading to significant performance improvements without substantial computational overhead. Notably, the introduction of frequency-based fine-tuning modules and novel optimizers tailored for incremental fine-tuning are setting new benchmarks in efficiency and accuracy. These advancements not only streamline the adaptation process but also pave the way for more versatile and robust models in domain generalization and multi-task learning scenarios.