ID-Sim：一种以身份为中心的相似度指标
ID-Sim: An Identity-Focused Similarity Metric
ID-Sim 提供了一个前馈式身份相似度指标，可直接用于个性化图像生成、身份检索与识别的一致性评估，替代 CLIP/ArcFace 等与人类判断偏差较大的代理指标；其用生成式合成数据构造受控身份/上下文变化来训练度量模型的做法，也可迁移到其他需要细粒度感知对齐的评测任务。
原文依据 we propose ID-Sim, a feed-forward metric designed to faithfully reflect human selective sensitivity
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论人类对身份具有显著的选择性敏感度——即使在视角、光照等上下文差异很大的情况下，也能轻松区分高度相似的身份。视觉模型一直难以达到这种能力，而个性化图像生成等以身份为中心的任务，其进展因缺乏以身份为中心的评测指标而受到拖累。为促进相关进展，我们提出 ID-Sim，一种前馈式指标，旨在忠实反映人类的选择性敏感度。为构建 ID-Sim，我们整理了一个高质量训练图像集，覆盖多样的真实世界领域，并辅以生成式合成数据，从而提供受控的、细粒度的身份与上下文变化。我们在一个新的统一评测基准上评估该指标，该基准用于衡量其在与人类标注一致性方面的表现，涵盖以身份为中心的识别、检索与生成任务。
英文原文摘要 928 字符
Humans have remarkable selective sensitivity to identities--they easily distinguish 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 towards 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.