Measuring And Improving Engagement of Text-to-Image Generation Models

📌 Engagement-Optimized Image Generation: We introduce the task of generating images optimized for user engagement, crucial in domains like advertising, fashion, and e-commerce.

📂 EngagingImageNet Dataset: A large-scale dataset with 168M tweets from 10,135 enterprise accounts (2007–2023), capturing image engagement metrics.

🖼️ EngageNet Model: A vision-language model trained to predict user engagement over images, outperforming common metrics like FID and aesthetics.

🏆 Engagement Arena: The first automated benchmarking platform for ranking text-to-image models based on engaging image generation.

🎯 Methods for Engagement Optimization of Text-to-Image Models: We optimize image generation for engagement via prompt conditioning, supervised fine-tuning, and RL-based alignment with EngageNet rewards.

BibTeX

                
            @inproceedings{
                khurana2025measuring,
                title={Measuring And Improving Engagement of Text-to-Image Generation Models},
                author={Varun Khurana and Yaman Kumar Singla and Jayakumar Subramanian and Changyou Chen and Rajiv Ratn Shah and zhiqiang xu and Balaji Krishnamurthy},
                booktitle={The Thirteenth International Conference on Learning Representations},
                year={2025},
                url={https://openreview.net/forum?id=TmCcNuo03f}
                }

Measuring And Improving Engagement of Text-to-Image Generation Models

ICLR 2025

Code

EngagingImageNet Dataset

Engagement Arena

BibTeX