Unmasked Token

Unmasked tokens are increasingly central to improving the efficiency and performance of self-supervised learning models, particularly in vision and language domains. Research focuses on leveraging unmasked tokens to enhance the contextual understanding of masked tokens within models like Vision Transformers (ViTs) and Masked Autoencoders (MAEs), leading to more efficient pre-training and improved downstream task performance. This approach contrasts with traditional masked-only methods, offering potential for significant computational savings and improved representation learning across various applications, including image classification, video generation, and text-based person re-identification. The ability to effectively utilize unmasked tokens represents a key advancement in self-supervised learning, driving progress in both model efficiency and accuracy.