Papers with Text-to-image models
The Face of Persuasion: Analyzing Bias and Generating Culture-Aware Ads (2025.findings-emnlp)
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| Challenge: | Text-to-image models are appealing for customizing visual ads and targeting specific populations. |
| Approach: | We examine the disparate level of persuasiveness of ads that are identical except for gender/race of the people portrayed. |
| Outcome: | The proposed technique is based on a demographic bias analysis of ads for different topics and a disparate level of persuasiveness of ads that are identical except for gender/race of the people portrayed. |
R-Bind: Unified Enhancement of Attribute and Relation Binding in Text-to-Image Diffusion Models (2025.emnlp-main)
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| Challenge: | Existing approaches to improve semantic binding require costly retraining or focus on only correctly generating attributes of entities. Existing methods focus on correctly generating attributes, ignoring the cruciality of correctly forming relations between entities. |
| Approach: | They propose a training-free method that improves both entity-attribute and entity-relation-entity binding by introducing three inference-time optimization losses that adjust attention maps during generation. |
| Outcome: | The proposed method improves both entity-attribute and entity-relation-entity binding without additional training. |
Self-Rewarding Large Vision-Language Models for Optimizing Prompts in Text-to-Image Generation (2025.findings-acl)
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| Challenge: | Existing methods for rewriting text-to-image models require specialized vocabulary . a new approach uses large vision language models to optimize text-based models . |
| Approach: | They propose a prompt optimization framework that rephrases a user prompt into a text-to-image model by using large vision language models as solver and reward model. |
| Outcome: | The proposed model outperforms existing models on two popular datasets. |