Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning

This work considers the problem of domain shift in person re-identification.Being trained on one dataset, a re-identification model usually performs much worse on unseen data. Partially this gap is caused by the relatively small scale of person re-identification datasets (compared to face recognition ones, for instance), but it is also related to training objectives... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Person Re-Identification MSMT17 OSNet-IAP 1.0x Rank-1 77.97 # 5
mAP 48.66 # 6

Methods used in the Paper


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