AI RESEARCH
MaSC: A Masked Similarity Metric for Evaluating Concept-Driven Generation
arXiv CS.CV
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ArXi:2605.22469v1 Announce Type: new Evaluating single-concept personalization in text-to-image diffusion requires measuring both concept preservation, which captures identity fidelity to a reference, and prompt following, which captures whether the generated scene matches the prompt. Existing metrics commonly compute these signals using global image or text-image embeddings, such as CLIP-I, DINO, and CLIP-T. We show that such metrics correlate poorly with human perception because they attend to the image as a whole instead of separating the concept subject from the background. We.