arXiv：cs.LG· Manyi Yao, Jurijs Nazarovs, Eunji Chong, Abhishek Sharma, Rohan Sarkar, Yue Guo, Christian R. Shelton, Amit K. Roy-Chowdhury, Debashish Pal·· 4 小时前AI 评分28
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
AI 导读
研究者提出身份条件化分数融合框架，无需训练即可为每个底库身份定制融合权重，通过对比身份内一致性与跨身份冒充者提取身份特征画像，并与查询条件自适应结合。在三个换装行人重识别基准上，该方法一致优于统计、排序和学习类基线，误非识别率最高绝对降低 8.8%。
正文
Abstract: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.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.07366 [cs.CV] |
| (or arXiv:2610.07366v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07366 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Manyi Yao [view email]
[v1]
Mon, 5 Oct 2026 20:34:49 UTC (4,157 KB)
来源：arXiv：cs.LG · arxiv.org