  Vision Transformers (ViTs) have emerged as popular models in computer vision,
demonstrating state-of-the-art performance across various tasks. This success
typically follows a two-stage strategy involving pre-training on large-scale
datasets using self-supervised signals, such as masked random patches, followed
by fine-tuning on task-specific labeled datasets with cross-entropy loss.
However, this reliance on cross-entropy loss has been identified as a limiting
factor in ViTs, affecting their generalization and transferability to
downstream tasks. Addressing this critical challenge, we introduce a novel
Label-aware Contrastive Training framework, LaCViT, which significantly
enhances the quality of embeddings in ViTs. LaCViT not only addresses the
limitations of cross-entropy loss but also facilitates more effective transfer
learning across diverse image classification tasks. Our comprehensive
experiments on eight standard image classification datasets reveal that LaCViT
statistically significantly enhances the performance of three evaluated ViTs by
up-to 10.78% under Top-1 Accuracy.
