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Jul, 2022
负样本数量很大:利用难度距离弹性损失进行再识别
Negative Samples are at Large: Leveraging Hard-distance Elastic Loss for Re-identification
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Hyungtae Lee, Sungmin Eum, Heesung Kwon
TL;DR
本文提出了一种动量关联(MoReID)框架,该框架使用大量负样本用于一般的重新识别任务,并提出了Hard-distance Elastic loss(HE损失)来最大化利用负样本集,实现了在三个重新识别基准测试中最先进的准确性。
Abstract
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 this framework is inspired by Mom
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