🤖 AI Summary
Existing methods for assessing the quality of AI-generated images struggle to balance accuracy and efficiency. To address this challenge, this work proposes a multi-level transfer framework based on knowledge distillation, which employs a teacher model with hybrid local–global processing and a lightweight student model relying solely on global features. Efficient representation transfer is achieved through multi-level feature distillation and fusion. Experimental results on four AIGIQA datasets demonstrate that the student model reduces computational overhead by 67.7% while maintaining evaluation performance comparable to that of the teacher model, significantly outperforming current state-of-the-art approaches.
📝 Abstract
With the rapid advancement of image generation technologies, perceptual quality assessment of AI-generated images has emerged as a crucial research direction in computer vision. The core challenge of this task lies in achieving efficient quality assessment for massive generated images. Current mainstream approaches exhibit two key limitations: 1) Methods employing complex feature extraction strategies, while improving performance, incur prohibitive computational costs that hinder real-time inference; 2) Simple image scaling-based solutions, despite their computational efficiency, demonstrate significantly inferior assessment accuracy. To address this critical issue, we propose Patch Knowledge Transfer (PKT), a knowledge distillation-based optimization framework that achieves synergistic optimization of visual representation capability and inference efficiency through an innovative multi-level knowledge transfer mechanism. Specifically, we design a dual-model architecture: a teacher model with local-global hybrid processing provides high-quality supervision signals, while a student model relying solely on global processing efficiently inherits the teacher's representation capacity through multi-level supervision. Extensive experiments conducted on 4 AIGIQA databases demonstrate that the PKT framework enables the student model to maintain performance comparable to the teacher while reducing computational costs by 67.7\%. Furthermore, compared to existing methods, our approach achieves a superior balance between model efficiency and assessment accuracy.