Self Supervised Vision Transformer

Self-supervised vision transformers (ViTs) aim to learn robust visual representations from unlabeled image data, eliminating the need for extensive manual annotation. Current research focuses on improving the efficiency and effectiveness of these models, often employing architectures like DINOv2 and exploring techniques such as masked image modeling and contrastive learning to enhance feature extraction for various downstream tasks, including image classification, object detection, and semantic segmentation. This approach holds significant promise for advancing numerous applications, from medical image analysis and remote sensing to industrial anomaly detection and even malware identification, by enabling the training of powerful models with limited labeled data.