论文标题
人通过主动样品挖掘重新识别
Person Re-Identification via Active Hard Sample Mining
论文作者
论文摘要
注释大规模图像数据集非常乏味，但对于培训人员重新识别模型来说是必要的。为了减轻这样的问题，我们通过培训有效的重新ID模型来提出一个主动的硬样品挖掘框架，并以最少的标签工作。考虑到硬样品可以提供信息的模式，我们首先制定了不确定性估算，以积极选择硬样品，以迭代从头开始训练重新ID模型。然后，多样性内估计旨在通过最大化其多样性来减少冗余硬样品。此外，我们提出了一个嵌入在主动硬样品挖掘框架中的计算机辅助身份建议模块，以帮助人类注释迅速准确地标记所选样品。进行了广泛的实验，以证明我们方法在几个公共数据集上的有效性。实验结果表明，我们的方法可以分别降低57％，63％和49％的注释工作，分别在1501，MSMT17和CUHK03上降低，同时最大程度地提高RE-ID模型的性能。
Annotating a large-scale image dataset is very tedious, yet necessary for training person re-identification models. To alleviate such a problem, we present an active hard sample mining framework via training an effective re-ID model with the least labeling efforts. Considering that hard samples can provide informative patterns, we first formulate an uncertainty estimation to actively select hard samples to iteratively train a re-ID model from scratch. Then, intra-diversity estimation is designed to reduce the redundant hard samples by maximizing their diversity. Moreover, we propose a computer-assisted identity recommendation module embedded in the active hard sample mining framework to help human annotators to rapidly and accurately label the selected samples. Extensive experiments were carried out to demonstrate the effectiveness of our method on several public datasets. Experimental results indicate that our method can reduce 57%, 63%, and 49% annotation efforts on the Market1501, MSMT17, and CUHK03, respectively, while maximizing the performance of the re-ID model.
