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

Varun Khurana; Yaman Kumar Singla; Jayakumar Subramanian; Changyou Chen; Rajiv Ratn Shah; Zhiqiang Xu; Balaji Krishnamurthy

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

Varun Khurana^*, Yaman Kumar Singla^*, Jayakumar Subramanian, Changyou Chen, Rajiv Ratn Shah, Zhiqiang Xu, Balaji Krishnamurthy

* Equal Contribution

Media and Data Science Research (MDSR) Lab, Adobe

ICLR 2025

Accepted at ICLR 2025

Get in touch with us at behavior-in-the-wild@googlegroups.com

Paper EngagementArena

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: We optimize image generation for engagement via prompt conditioning, supervised fine-tuning, and RL-based alignment with EngageNet rewards.

Engagement Arena

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}
}

Acknowledgement

We thank Adobe for their generous sponsorship.