基于互均值学习和gan的无监督跨域自适应人物再识别

Leethar Yao, Bo-Yu Lin, Qazi Mazhar ul Haq, Ihtesham Ul Islam
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引用次数: 0

摘要

由于目标领域标签的不可用性,无监督跨领域自适应是一项具有挑战性的任务。在现有的方法中,基于伪标签的方法具有相当的性能,但大多数方法使用的是没有标签的目标域数据,这给目标模型学习到足够的特征带来了挑战。在本文中,我们使用基于生成的模型来生成更多的目标数据。相互学习模型与生成模型合作,将一个模型的知识转移到另一个模型,最终提高模型的整体性能。在Duke和Market数据集上进行了广泛的实验,与最先进的方法相比,显著提高了性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised Cross-Domain Adaptation through Mutual Mean Learning and GANs for Person Re-identification
Unsupervised cross-domain adaptation is a challenging task for person re-identification due to the unavailability of target domain labels. Among existing methods, pseudo-Iabels-based methods have considerable performance but most of them use target domain data without labels which are challenging difficult for the target model to learn enough features. In this paper, we use generative based models that generate more target data. In cooperation with the generative model, a mutual learning model is used to transfer knowledge of one model to another model that ultimately improves overall model performance. Ex-tensive experiments are performed on Duke and Market datasets that significantly achieve improved performance in comparison to state-of-the-art methods.
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