CAP: Evaluation of Persuasive and Creative Image Generation

TL;DR

This paper introduces three novel metrics to evaluate creativity, alignment, and persuasiveness in generated advertisement images, addressing gaps in current evaluation methods and proposing improvements for Text-to-Image models.

Contribution

The paper presents new evaluation metrics for assessing creativity, alignment, and persuasiveness in generated images and proposes an approach to improve T2I model performance in these aspects.

Findings

Current T2I models struggle with implicit prompts.

Models have difficulty generating creative and persuasive images.

Proposed approach enhances alignment, creativity, and persuasiveness.

Abstract

We address the task of advertisement image generation and introduce three evaluation metrics to assess Creativity, prompt Alignment, and Persuasiveness (CAP) in generated advertisement images. Despite recent advancements in Text-to-Image (T2I) generation and their performance in generating high-quality images for explicit descriptions, evaluating these models remains challenging. Existing evaluation methods focus largely on assessing alignment with explicit, detailed descriptions, but evaluating alignment with visually implicit prompts remains an open problem. Additionally, creativity and persuasiveness are essential qualities that enhance the effectiveness of advertisement images, yet are seldom measured. To address this, we propose three novel metrics for evaluating the creativity, alignment, and persuasiveness of generated images. Our findings reveal that current T2I models struggle with creativity, persuasiveness, and alignment when the input text is implicit messages. We further introduce a simple yet effective approach to enhance T2I models' capabilities in producing images that are better aligned, more creative, and more persuasive.