mlsanityData to 7 Oct 2026

ID-Sim: An Identity-Focused Similarity Metric

arXiv 2604.05039 ↗First posted 6 Apr 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Humans have remarkable selective sensitivity to identities -- easily distinguishing between highly similar identities, even across significantly different contexts such as diverse viewpoints or lighting. Vision models have struggled to match this capability, and progress toward identity-focused tasks such as personalized image generation is slowed by a lack of identity-focused evaluation metrics. To help facilitate progress, we propose ID-Sim, a feed-forward metric designed to faithfully reflect human selective sensitivity. To build ID-Sim, we curate a high-quality training set of images spanning diverse real-world domains, augmented with generative synthetic data that provides controlled, fine-grained identity and contextual variations. We evaluate our metric on a new unified evaluation benchmark for assessing consistency with human annotations across identity-focused recognition, retrieval, and generative tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 69 of its 109 references that are in the map. First posted on arXiv 6 Apr 2026.

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