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Re-identification (re-ID) is currently investigated as a closed-world imageretrieval task, and evaluated by retrieval based metrics. The algorithms returnranking lists to users, but cannot tell which images are the true target. Inessence, current re-ID overemphasizes the importance of retrieval butunderemphasizes that of verification, \textit{i.e.}, all returned images areconsidered as the target. On the other hand, re-ID should also include thescenario 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 retrievaland 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 verificationinto a single unified metric. It can also be decomposed into a family ofsub-metrics, enabling a clear analysis of re-ID performance. We evaluate theeffectiveness of GOM on the re-ID benchmarks, showing its ability to captureimportant aspects of re-ID performance that have not been taken into account byestablished metrics so far. Furthermore, we show GOM scores excellent inaligning with human visual evaluation of re-ID performance. Related codes areavailable at https://github.com/YuanXinCherry/Person-reID-Evaluation

Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric