Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT

• URL: http://arxiv.org/abs/2305.00201v1
• Date: Sat, 29 Apr 2023 08:59:12 GMT
• Title: Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
• Authors: Zhenxiang Xiao, Yuzhong Chen, Lu Zhang, Junjie Yao, Zihao Wu, Xiaowei Yu, Yi Pan, Lin Zhao, Chong Ma, Xinyu Liu, Wei Liu, Xiang Li, Yixuan Yuan, Dinggang Shen, Dajiang Zhu, Tianming Liu, Xi Jiang
• Abstract summary: In this paper, we focus on adapting prompt design based on instruction tuning into a visual transformer model for image classification. The key idea is to implement multi-modal prompts related to category information to guide the fine-tuning of the model. Based on the experiments of several image captionining tasks, the performance and domain adaptability were improved.
• Score: 58.70209492842953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks. In this paper, we focus on adapting prompt design based on instruction tuning into a visual transformer model for image classification which we called Instruction-ViT. The key idea is to implement multi-modal prompts (text or image prompt) related to category information to guide the fine-tuning of the model. Based on the experiments of several image captionining tasks, the performance and domain adaptability were improved. Our work provided an innovative strategy to fuse multi-modal prompts with better performance and faster adaptability for visual classification models.

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