Pre Trained ViT

Pre-trained Vision Transformers (ViTs) are large, powerful models trained on massive datasets to learn general visual representations, which are then fine-tuned for specific downstream tasks like image classification, segmentation, and object tracking. Current research focuses on improving efficiency (e.g., through lightweight adapters and dynamic tuning), enhancing robustness to adversarial attacks, and exploring novel training methods such as masked image modeling and self-supervised learning. These advancements are significantly impacting computer vision, enabling more accurate and resource-efficient applications across diverse fields, including medical image analysis and mobile device deployment.