Abstract
Regular unsupervised domain adaptive person re-identification (ReID) focuses on adapting a model from a source domain to a fixed target domain. However, an adapted ReID model can hardly retain previously-acquired knowledge and generalize to unseen data. In this paper, we propose a Dual-level Joint Adaptation and Anti-forgetting (DJAA) framework, which incrementally adapts a model to new domains without forgetting source domain and each adapted target domain. We explore the possibility of using prototype and instance-level consistency to mitigate the forgetting during the adaptation. Specifically, we store a small number of representative image samples and corresponding cluster prototypes in a memory buffer, which is updated at each adaptation step. With the buffered images and prototypes, we regularize the image-to-image similarity and image-to-prototype similarity to rehearse old knowledge. After the multi-step adaptation, the model is tested on all seen domains and several unseen domains to validate the generalization ability of our method. Extensive experiments demonstrate that our proposed method significantly improves the anti-forgetting, generalization and backward-compatible ability of an unsupervised person ReID model.
Abstract (translated)
常规的无监督领域适应性人员重新识别（ReID）主要集中在将模型从源域迁移到固定的靶域上。然而，经过适应后的ReID模型很难保持之前获得的知识，并且难以推广到未见过的数据中。在本文中，我们提出了一种双重级联适应与抗遗忘(DJAA)框架，该框架能够在不忘记源域和每个已适应的靶域知识的情况下，逐步地将模型迁移到新的领域上。我们探索了利用原型和实例层次的一致性来减轻适应过程中遗忘的可能性。具体来说，我们将少量代表性图像样本及其对应的聚类原型存储在一个内存缓冲区中，并在每次适应步骤时更新该缓冲区。借助于缓存的图像与原型，我们通过调节图像到图像相似性和图像到原型相似性来复习旧知识。经过多步适应后，模型将在所有见过的域和一些未见的域上进行测试以验证方法的泛化能力。大量的实验表明，我们提出的方法显著提高了无监督人员ReID模型的抗遗忘、泛化及向后兼容的能力。
URL
https://arxiv.org/abs/2411.14695
