LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models

• URL: http://arxiv.org/abs/2309.01155v2
• Date: Fri, 22 Sep 2023 02:46:55 GMT
• Title: LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models
• Authors: Cheng Shi and Sibei Yang
• Abstract summary: We show that synthetic text images are good visual prompts for vision-language models! We propose our LoGoPrompt, which reformulates the classification objective to the visual prompt selection. Our method consistently outperforms state-of-the-art methods in few-shot learning, base-to-new generalization, and domain generalization.
• Score: 28.983503845298824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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