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Summary

This summary is machine-generated.

A new framework, TPMS, improves AI-generated image quality assessment by using task-specific prompts and multi-level similarity. This enhances both perceptual quality and text-to-image alignment for better generative models.

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Area of Science:

Artificial Intelligence
Computer Vision
Machine Learning

Background:

AI-generated images (AIGIs) are gaining attention but suffer from poor quality and prompt misalignment.
Current evaluation methods use generic prompts and coarse similarity, limiting performance.

Purpose of the Study:

To develop an advanced framework for assessing AIGI perceptual quality and text-to-image alignment.
To overcome limitations of existing task-agnostic evaluation methods.

Main Methods:

Proposed TPMS framework with task-specific prompts for perceptual and alignment quality.
Implemented multi-granularity similarity: coarse-level with task-specific prompts and fine-level with initial prompts.

Main Results:

TPMS demonstrated superior performance in AIGI quality prediction.
Extensive experiments on four benchmarks confirmed the framework's effectiveness.

Conclusions:

The proposed TPMS framework offers precise and robust AIGI quality assessment.
Task-specific prompts and multi-granularity similarity are key to improving generative model evaluation.