most citedNegative Samples are at Large: Leveraging Hard-distance Elastic Loss for Re-identification

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cs.CV2022★ 1 cited

Negative Samples are at Large: Leveraging Hard-distance Elastic Loss for Re-identification

Hyungtae Lee, Sungmin Eum, Heesung Kwon

We present a Momentum Re-identification (MoReID) framework that can leverage a very large number of negative samples in training for general re-identification task. The design of t…