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Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images
May 16, 2024, 4:45 a.m. | Memoona Aziz (Western University, Canada), Umair Rehman (Western University, Canada), Muhammad Umair Danish (Western University, Canada), Katarina Gro
cs.CV updates on arXiv.org arxiv.org
Abstract: This paper introduces the Global-Local Image Perceptual Score (GLIPS), an image metric designed to assess the photorealistic image quality of AI-generated images with a high degree of alignment to human visual perception. Traditional metrics such as FID and KID scores do not align closely with human evaluations. The proposed metric incorporates advanced transformer-based attention mechanisms to assess local similarity and Maximum Mean Discrepancy (MMD) to evaluate global distributional similarity. To evaluate the performance of GLIPS, …
abstract ai-generated images alignment arxiv cs.cv generated global human image images kid metrics paper perception photorealistic quality type visual
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