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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.05039 |
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| _version_ | 1866915919036416000 |
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| author | Chae, Julia Kolkin, Nicholas Wang, Jui-Hsien Zhang, Richard Beery, Sara Ham, Cusuh |
| author_facet | Chae, Julia Kolkin, Nicholas Wang, Jui-Hsien Zhang, Richard Beery, Sara Ham, Cusuh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05039 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ID-Sim: An Identity-Focused Similarity Metric Chae, Julia Kolkin, Nicholas Wang, Jui-Hsien Zhang, Richard Beery, Sara Ham, Cusuh Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | ID-Sim: An Identity-Focused Similarity Metric |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2604.05039 |