AI RESEARCH
Redefining Quality Criteria and Distance-Aware Score Modeling for Image Editing Assessment
arXiv CS.CV
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ArXi:2604.12175v1 Announce Type: new Recent advances in image editing have heightened the need for reliable Image Editing Quality Assessment (IEQA). Unlike traditional methods, IEQA requires complex reasoning over multimodal inputs and multi-dimensional assessments. Existing MLLM-based approaches often rely on human heuristic prompting, leading to two key limitations: rigid metric prompting and distance-agnostic score modeling. These issues hinder alignment with implicit human criteria and fail to capture the continuous structure of score spaces.