Visual Tuning

Visual tuning focuses on efficiently adapting large pre-trained visual models to specific downstream tasks, minimizing computational costs and maximizing performance. Current research emphasizes parameter-efficient tuning methods, such as adapter tuning, prompt tuning, and low-rank adaptation, which update only a small subset of the model's parameters instead of the entire network. These techniques aim to bridge the gap between the performance of full fine-tuning and the resource constraints of deploying large models on edge devices, improving the practicality of using powerful pre-trained models for various applications. This research area is significant for advancing computer vision applications by enabling efficient deployment of state-of-the-art models across diverse hardware and data limitations.