Can text teach vision models to see better?

TextTeacher injects semantic knowledge from language models into vision training by using image captions and a frozen text encoder to guide representation learning. The method adds a lightweight auxiliary loss during training but leaves the final model unchanged, improving ImageNet accuracy by up to 2.7% on ViT while outperforming standard knowledge distillation. The approach requires no expensive multimodal training and transfers well to downstream tasks.