Core Concepts
Collage Prompting offers a cost-effective approach for image recognition with GPT-4V.
Abstract
The content introduces Collage Prompting as a budget-friendly method for image recognition using GPT-4V. It discusses the financial challenges associated with GPT-4V's inference costs and proposes Collage Prompting as a solution to reduce expenses while maintaining accuracy. The method involves concatenating multiple images into a single visual prompt, optimizing the arrangement of images to enhance recognition accuracy. Experimental results demonstrate the effectiveness of Collage Prompting in reducing costs and improving accuracy compared to standard prompting methods.
Structure:
Introduction to Generative AI and Large Language Models (LLMs)
Proposal of Collage Prompting for Cost-Efficient Image Recognition with GPT-4V
Methodology: Learning to Collage Prompt (LCP) Algorithm
Experiment Results on Various Datasets and Comparison Metrics (CER, PCE)
Cost Analysis and Comparison with Traditional Models
Ablation Study on Optimization Methods and Case Study Visualization
Stats
画像認識におけるコスト効率の高い方法として、Collage Promptingが提案されています。
2×2および3×3のグリッド配置を最適化することで、精度を向上させながらコストを削減します。