Identity-Conditioned Score Fusion for Open-Set Person Re-Identification
Read the original on arXiv Machine Learning →The paper proposes identity-conditioned score fusion for open-set person re-identification, a framework that assigns weights tailored to each gallery identity without requiring additional training. By comparing intra-identity consistency with cross-identity impostors, it derives identity-specific profiles that work with query-conditioned adaptation through a parameter-free rule, enhancing the separation between true and false matches while maintaining score calibration. Experiments on three clothes-changing benchmarks show consistent improvements over statistical, rank-based, and learned baselines, achieving up to an 8.8% absolute reduction in the false non-identification rate.
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