ID-Sim is a new similarity metric that aims to capture human selective sensitivity to identities by training on curated real and generative synthetic data and validating against human annotations on recognition, retrieval, and generative tasks.
These prompts define the discrete levels of background variation and illumination used to construct the edit grids in Figures 26 and 28
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ID-Sim: An Identity-Focused Similarity Metric
ID-Sim is a new similarity metric that aims to capture human selective sensitivity to identities by training on curated real and generative synthetic data and validating against human annotations on recognition, retrieval, and generative tasks.