Abstract
Unsupervised person re-identification (Re-ID) methods consist of training with a carefully labeled source dataset, followed by generalization to an unlabeled target dataset, i.e. person-identity information is unavailable. Inspired by domain adaptation techniques, these methods avoid a costly, tedious and often unaffordable labeling process. This paper investigates the use of camera-index information, namely which camera captured which image, for unsupervised person Re-ID. More precisely, inspired by domain adaptation adversarial approaches, we develop an adversarial framework in which the output of the feature extractor should be useful for person Re-ID and in the same time should fool a camera discriminator. We refer to the proposed method as camera adversarial transfer (CAT). We evaluate adversarial variants and, alongside, the camera robustness achieved for each variant. We report cross-dataset ReID performance and we compare the variants of our method with several state-of-the-art methods, thus showing the interest of exploiting camera-index information within an adversarial framework for the unsupervised person Re-ID.
Abstract (translated)
无监督人员重新识别（RE ID）方法包括使用仔细标记的源数据集进行培训，然后泛化为未标记的目标数据集，即人员身份信息不可用。受领域适应技术的启发，这些方法避免了一个昂贵、乏味且常常难以负担的标记过程。本文研究了摄像机索引信息的使用，即哪些摄像机捕获了哪些图像，用于无人监视的人的RE-ID。更准确地说，在适应域的对抗方法的启发下，我们开发了一个对抗性框架，在该框架中，特征抽取器的输出应该对人的RE-ID有用，同时应该照相机鉴别器。我们将建议的方法称为摄像机对抗传输（CAT）。我们评估了对手的变种，以及每个变种的摄像机的鲁棒性。我们报告了交叉数据集REID的性能，并将我们的方法的变体与几种最先进的方法进行了比较，从而显示出在对抗性框架内利用摄像机索引信息为无人监督的人重新识别的兴趣。
URL
https://arxiv.org/abs/1904.01308
