  Prompt engineering is a powerful tool used to enhance the performance of
pre-trained models on downstream tasks. For example, providing the prompt
"Let's think step by step" improved GPT-3's reasoning accuracy to 63% on
MutiArith while prompting "a photo of" filled with a class name enables CLIP to
achieve $80$\% zero-shot accuracy on ImageNet. While previous research has
explored prompt learning for the visual modality, analyzing what constitutes a
good visual prompt specifically for image recognition is limited. In addition,
existing visual prompt tuning methods' generalization ability is worse than
text-only prompting tuning. This paper explores our key insight: synthetic text
images are good visual prompts for vision-language models! To achieve that, we
propose our LoGoPrompt, which reformulates the classification objective to the
visual prompt selection and addresses the chicken-and-egg challenge of first
adding synthetic text images as class-wise visual prompts or predicting the
class first. Without any trainable visual prompt parameters, experimental
results on 16 datasets demonstrate that our method consistently outperforms
state-of-the-art methods in few-shot learning, base-to-new generalization, and
domain generalization.
