论文标题
重新识别=检索 +验证：返回本质，并用一个新指标向前
Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric
论文作者
论文摘要
重新识别（RE-ID）目前已作为封闭世界图像检索任务进行研究，并通过基于检索的指标进行评估。该算法将排名列表返回给用户，但无法确定哪些图像是真正的目标。从本质上讲，当前的重新ID过分强调了检索的重要性，但强调了验证的强调，\ textit {i.e。}，所有返回的图像都被视为目标。另一方面，Re-ID还应包括查询身份在画廊中没有出现的情况。为此，我们回到了re-id的本质，\ textit {i.e。}，在开放式设定设置中的检索和验证的组合，并提出了一个新的度量标准，即真正的开放式Re-ID衡量标准（GOM）。 GOM明确平衡了将检索和验证进行单个统一度量的效果。它也可以将其分解为一组次级计量，从而可以清楚地分析重新ID性能。我们评估了GOM对重新ID基准测试的有效性，显示了其捕获重新效果的重要方面的能力，而重新ID绩效的重要方面尚未考虑到迄今为止尚未确定的指标。此外，我们在与重新ID性能的人类视觉评估方面表现出了出色的GOM分数。相关代码可从https://github.com/yuanxincherry/person-reid-evaluation获得
Re-identification (re-ID) is currently investigated as a closed-world image retrieval task, and evaluated by retrieval based metrics. The algorithms return ranking lists to users, but cannot tell which images are the true target. In essence, current re-ID overemphasizes the importance of retrieval but underemphasizes that of verification, \textit{i.e.}, all returned images are considered as the target. On the other hand, re-ID should also include the scenario that the query identity does not appear in the gallery. To this end, we go back to the essence of re-ID, \textit{i.e.}, a combination of retrieval and verification in an open-set setting, and put forward a new metric, namely, Genuine Open-set re-ID Metric (GOM). GOM explicitly balances the effect of performing retrieval and verification into a single unified metric. It can also be decomposed into a family of sub-metrics, enabling a clear analysis of re-ID performance. We evaluate the effectiveness of GOM on the re-ID benchmarks, showing its ability to capture important aspects of re-ID performance that have not been taken into account by established metrics so far. Furthermore, we show GOM scores excellent in aligning with human visual evaluation of re-ID performance. Related codes are available at https://github.com/YuanXinCherry/Person-reID-Evaluation
