arXiv:2411.02437v1 Announce Type: new Abstract: Evaluating text-to-image generative models remains a challenge, despite the remarkable progress being made in their overall performances. While existing metrics like CLIPScore work for coarse evaluations, they lack the sensitivity to distinguish finer differences as model performance rapidly improves. In this work, we focus on the text rendering aspect of these models, which provides a lens for evaluating a generative model’s fine-grained instruction-following capabilities. To this end, we introduce a new evaluation framework called TypeScore to sensitively assess a model’s ability to generate images with high-fidelity embedded text by following precise instructions. We argue that this text generation capability serves as a proxy for general instruction-following ability in image synthesis. TypeScore uses an additional image description model and leverages an ensemble dissimilarity measure between the original and extracted text to evaluate the fidelity of the rendered text. Our proposed metric demonstrates greater resolution than CLIPScore to differentiate popular image generation models across a range of instructions with diverse text styles. Our study also evaluates how well these vision-language models (VLMs) adhere to stylistic instructions, disentangling style evaluation from embedded-text fidelity. Through human evaluation studies, we quantitatively meta-evaluate the effectiveness of the metric. Comprehensive analysis is conducted to explore factors such as text length, captioning models, and current progress towards human parity on this task. The framework provides insights into remaining gaps in instruction-following for image generation with embedded text.
The article “Evaluating Text-to-Image Generative Models: Introducing TypeScore for Fine-Grained Instruction-Following” addresses the challenge of evaluating text-to-image generative models and highlights the limitations of existing metrics in distinguishing finer differences in model performance. The focus of this work is on the text rendering aspect of these models, which serves as a lens for evaluating their fine-grained instruction-following capabilities. To address this evaluation gap, the authors introduce a new framework called TypeScore, which assesses a model’s ability to generate images with high-fidelity embedded text by following precise instructions. They argue that this text generation capability can be seen as a proxy for general instruction-following ability in image synthesis. TypeScore utilizes an additional image description model and leverages an ensemble dissimilarity measure to evaluate the fidelity of the rendered text. The proposed metric demonstrates greater resolution than existing metrics in differentiating popular image generation models across various instructions and text styles. The study also evaluates how well these vision-language models adhere to stylistic instructions, separating style evaluation from embedded-text fidelity. Human evaluation studies are conducted to quantitatively assess the effectiveness of the metric. The framework provides insights into the remaining gaps in instruction-following for image generation with embedded text, considering factors such as text length, captioning models, and progress towards human parity on this task.
Evaluating Text-to-Image Generative Models: An Innovative Approach
Text-to-image generative models have shown remarkable progress in their overall performances. However, evaluating these models accurately poses a challenge. While existing metrics like CLIPScore provide a coarse evaluation, they lack the sensitivity to distinguish finer differences as model performance rapidly improves.
In this work, we propose a new evaluation framework called TypeScore, which focuses on the text rendering aspect of these models. We believe that text generation capability serves as a proxy for general instruction-following ability in image synthesis. By assessing a model’s ability to generate high-fidelity images with embedded text by following precise instructions, we can gain insights into its fine-grained instruction-following capabilities.
TypeScore introduces an additional image description model and leverages an ensemble dissimilarity measure between the original and extracted text to evaluate the fidelity of the rendered text. Compared to CLIPScore, our proposed metric demonstrates greater resolution in differentiating popular image generation models across a wide range of instructions with diverse text styles.
Furthermore, our study delves into how well these vision-language models (VLMs) adhere to stylistic instructions, allowing us to disentangle style evaluation from embedded-text fidelity. Through human evaluation studies, we quantitatively meta-evaluate the effectiveness of the TypeScore metric.
We also conduct a comprehensive analysis to explore various factors that can impact the performance of text-to-image generative models, including text length, captioning models, and the current progress towards achieving human parity on this task. This analysis helps to identify remaining gaps in instruction-following for image generation with embedded text.
The TypeScore framework provides valuable insights into the strengths and limitations of text-to-image generative models. By focusing on the crucial aspect of text rendering, we can better evaluate the instruction-following capabilities of these models and drive further advancements in this field.
The paper titled “Evaluating Text-to-Image Generative Models using TypeScore” addresses the challenges in evaluating the performance of text-to-image generative models. While these models have made significant progress in overall performance, existing metrics like CLIPScore are not sensitive enough to distinguish finer differences as model performance improves rapidly.
To overcome this limitation, the authors propose a new evaluation framework called TypeScore, which focuses specifically on the text rendering aspect of generative models. They argue that a model’s ability to generate images with high-fidelity embedded text demonstrates its fine-grained instruction-following capabilities. In other words, the text generation capability serves as a proxy for the model’s general instruction-following ability in image synthesis.
TypeScore utilizes an additional image description model and leverages an ensemble dissimilarity measure between the original text and the extracted text from the generated image to evaluate the fidelity of the rendered text. By comparing the fidelity of text generated by different models across a range of instructions with diverse text styles, the proposed metric demonstrates greater resolution than CLIPScore.
Furthermore, the study also explores how well these vision-language models (VLMs) adhere to stylistic instructions, effectively separating style evaluation from embedded-text fidelity. Human evaluation studies are conducted to quantitatively assess the effectiveness of the TypeScore metric. The analysis considers factors such as text length, captioning models, and the progress made towards achieving human parity on this task.
Overall, this research provides valuable insights into the remaining gaps in instruction-following for image generation with embedded text. By introducing a more sensitive evaluation framework, TypeScore allows for a more nuanced assessment of text-to-image generative models’ performance, enabling researchers to better understand and improve these models’ instruction-following capabilities.
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