【摘要】 近年来，无人监督行人重识别技术取得了长足的进步。该技术从大量未标记行人数据库中检索感兴趣的行人在不同相机下的图片。然而，目前的研究还存在一些问题，例如跨摄像头行人图片的影响和伪标签噪声等。为了解决这些问题，本文从两个方面进行研究：消除相机偏差和动态更新记忆模型。在去除相机偏差方面，基于一个可学习的通道注意力模块从特征图中提取仅与摄像头相关的特征，从而消除全局特征中的相机偏差，得到可以代表行人的鲁棒特征。在动态更新记忆模型方面，由于实例特征不一定属于伪标签所标识的行人类别，因此本文采用一种基于实例特征与类别特征之间距离的方式动态更新记忆模型，使类别特征趋于真实特征。我们将相机偏差去除和记忆模型动态更新结合起来，以更好地解决这些问题。大量实验表明，本文提出的方法在无监督行人重新识别任务中的性能优于其他方法。更多还原

【Abstract】 In recent years, unsupervised person reidentification technology has made great strides. The technology retrieves images of interested persons under different cameras from massive repositories of unlabeled images. However, in the current research, there are some existing problems, such as the influence of pedestrians appearing across cameras and pseudo-label noise. To solve these problems, we conduct research in two ways: removing the camera bias and dynamically updating the memory model. In removing the camera bias, based on a learnable channel attention module, the features that are only related to cameras can be extracted from the feature map, thereby removing the camera bias in the global features and obtaining the features that can represent the pedestrians. In regards to dynamically updating the memory model,since the instance features do not necessarily belong to the identity represented by the pseudo-label, we adopt a method to update the memory dynamically according to the distance between the instance features and the category features so that the category features tend to be true. We combine the removal of the camera bias and the dynamic updating of the memory model to better solve problems in this field. Extensive experimentation demonstrates the superiority of our method over the state-of-the-art approaches on fully unsupervised Re-ID tasks.更多还原