TextInVision: Text and Prompt Complexity Driven Visual Text Generation Benchmark
Project Overview
The document explores the role of generative AI in education, emphasizing the creation of TextInVision, a benchmark designed to evaluate generative AI models specifically for visual text generation. It addresses the limitations of current text-to-image models, which often struggle with accurately embedding text in images, leading to issues like spelling errors and lack of contextual relevance. The benchmark seeks to provide a thorough assessment of these models' capabilities through a variety of prompts and texts that mirror real-world educational and advertising scenarios. By identifying these challenges, the document underscores the necessity for advancements in model architectures to improve text fidelity and enhance the overall quality of generated images that incorporate text. These developments have significant implications for educational applications, where accurate and contextually relevant visual representations can facilitate better learning experiences.
Key Applications
TextInVision Benchmark
Context: Evaluating text-to-image generation models for educational materials
Implementation: Developed a large-scale benchmark with varying prompt complexities and text attributes to assess model performance
Outcomes: Identified common errors in text embedding and established a new standard for evaluating generative models
Challenges: Models struggle with accurately generating specific visual text, particularly longer or unique text elements
Implementation Barriers
Technical
Existing models exhibit difficulties in rendering accurate visual text, especially with longer phrases or specialized terms. There is a lack of standardized benchmarks to assess text embedding capabilities in generated images.
Proposed Solutions: Introducing a comprehensive benchmark like TextInVision to systematically evaluate and improve model performance. TextInVision provides a detailed framework for evaluating performance across diverse real-world scenarios.
Project Team
Forouzan Fallah
Researcher
Maitreya Patel
Researcher
Agneet Chatterjee
Researcher
Vlad I. Morariu
Researcher
Chitta Baral
Researcher
Yezhou Yang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Forouzan Fallah, Maitreya Patel, Agneet Chatterjee, Vlad I. Morariu, Chitta Baral, Yezhou Yang
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai