身份条件化分数融合用于开放集行人重识别
Identity-Conditioned Score Fusion for Open-Set Person Re-Identification
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中文总结 AI 辅助
提出身份条件化分数融合框架，无需训练即可为每个库身份定制权重，通过对比身份内一致性与跨身份冒充者提取身份画像，在三个换衣行人重识别基准上优于基线，误非识别率最高降低8.8%。
中文摘要 AI 辅助
鲁棒的行人重识别通常结合互补线索，如人脸、步态和体型。虽然自适应融合通常针对查询质量，但模型强度也因身份而异。我们引入了身份条件化分数融合，这是一个无需训练即可为每个库身份定制权重的框架。通过对比身份内一致性与跨身份冒充者，它提取出特定于身份的画像，并通过无参数规则与查询条件化适应相结合。这扩大了真实匹配与虚假匹配之间的分离，同时保持了分数校准。在三个换衣行人重识别基准上的评估表明，我们的方法始终优于统计、基于排名和学习的基线，实现了误非识别率最高8.8%的绝对降低，并证明了身份条件化融合在开放集行人重识别中的价值。
英文摘要
Robust person re-identification often combines complementary cues such as face, gait, and body shape. While adaptive fusion typically targets query quality, model strength also varies across identities. We introduce identity-conditioned score fusion, a framework that tailors weights to each gallery identity without training. By contrasting intra-identity consistency against cross-identity impostors, it extracts identity-specific profiles that couple with query-conditioned adaptation via a parameter-free rule. This widens the separation between true and false matches while preserving score calibration. Evaluations on three clothes-changing person re-identification benchmarks show that our method consistently outperforms statistical, rank-based, and learned baselines, achieving up to an 8.8% absolute reduction in the false non-identification rate and demonstrating the value of identity-conditioned fusion in open-set person re-identification.
发表机构
- Amazon（亚马逊）
- University of California, Riverside（加州大学河滨分校）
机构由 AI 辅助整理，请以论文原文为准。
