论文标题
通过对比度排练重新识别无监督的终身人士
Unsupervised Lifelong Person Re-identification via Contrastive Rehearsal
论文作者
论文摘要
现有的无监督人员重新识别（REID）方法着重于调整对源域上训练的模型，以适应固定目标域。但是，改编的REID模型通常只能在某个目标域上运行良好，但几乎无法记住源域知识并普遍以即将看到的看不见的数据。在本文中，我们提出了无监督的终身人物里德（Reid），该人的重点是在不忘记从旧领域中学到的知识的情况下不断地对新领域进行无监督的领域适应。为了解决无监督的终身性Reid，我们对少数存储的旧样品进行了对比彩排，同时依次适应了新的域。我们进一步在旧模型和新模型之间设置了图像到图像相似性约束，以适合旧知识的方式将模型更新正规化。我们以无监督的方式在几个大型数据集上依次训练我们的模型，并在所有看到的域以及几个看不见的域上对其进行测试，以验证我们方法的普遍性。我们提出的无监督的终身方法实现了强大的普遍性，这显着超过了对可见和看不见的域的先前终身方法。代码将在https://github.com/chenhao2345/ucr上提供。
Existing unsupervised person re-identification (ReID) methods focus on adapting a model trained on a source domain to a fixed target domain. However, an adapted ReID model usually only works well on a certain target domain, but can hardly memorize the source domain knowledge and generalize to upcoming unseen data. In this paper, we propose unsupervised lifelong person ReID, which focuses on continuously conducting unsupervised domain adaptation on new domains without forgetting the knowledge learnt from old domains. To tackle unsupervised lifelong ReID, we conduct a contrastive rehearsal on a small number of stored old samples while sequentially adapting to new domains. We further set an image-to-image similarity constraint between old and new models to regularize the model updates in a way that suits old knowledge. We sequentially train our model on several large-scale datasets in an unsupervised manner and test it on all seen domains as well as several unseen domains to validate the generalizability of our method. Our proposed unsupervised lifelong method achieves strong generalizability, which significantly outperforms previous lifelong methods on both seen and unseen domains. Code will be made available at https://github.com/chenhao2345/UCR.
