Year: 2024
Type: conference-paper
Abstract: The ability to predict the performance of a query before results are returned has been a longstanding challenge in Information Retrieval (IR) systems. Inspired by this task, we introduce, in this paper, a novel task called “Prompt Performance Prediction” that aims to predict the performance of a prompt, before obtaining the actual generated images. We demonstrate the plausibility of our task by measuring the correlation coefficient between predicted and actual performance scores across: three datasets containing pairs of prompts and generated images and three art domain datasets of real images and real user appreciation ratings. Our results show promising performance prediction capabilities, suggesting potential applications for optimizing user prompts.
Institution: École Normale Supérieure - PSL
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4402916077