Prompt Recommendations for AI Art

Publication Date

1-1-2023

Document Type

Conference Proceeding

Publication Title

Proceedings - 2023 IEEE 6th International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023

DOI

10.1109/AIKE59827.2023.00017

First Page

62

Last Page

65

Abstract

One of the main areas where generative AI models thrive is image synthesis or generation. This work highlights the importance of quality prompts in generating compelling artworks and delves into four principal methodologies for generating prompt recommendations: text embeddings, ensemble models, text with image embeddings and object detection for feature extraction. Multiple traditional and neural network-based models are explored for feature vector representation. Furthermore, the study explores the incorporation of image embeddings, the user s preferred art styles for tailored recommendations, and the inherent challenges in evaluating these systems. We also propose a novel methodology for evaluating such systems, in the absence of ratings or preference scores, using graph analysis and community detection algorithms. This work distinctly contributes to the prompt recommendation domain and complements previous works in the AI art generation landscape.

Keywords

AI Art Generation, Community detection, Prompt Engineering, Prompt Recommendation System, Text Embeddings, Text-Image Embeddings

Department

Computer Engineering

Link to Full Text

Share

COinS