论文标题
全面记忆以适应性地学习：无监督的跨域的人与多层次记忆
Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory
论文作者
论文摘要
无监督的跨域重新识别（RE-ID）旨在使标记的源域的信息调整为未标记的目标域。由于目标域缺乏监督，因此至关重要的是确定目标域中未标记的样本之间的基本相似性和差异性关系。为了在迷你批次培训中有效地使用整个数据关系，我们应用一系列内存模块来维护整个数据集的最新表示。与以前的作品中的简单示例内存不同，我们提出了一个新颖的多级内存网络（MMN），以发现目标域中的多级互补信息，依靠三个内存模块，即零件级内存，实例级记忆，实例级记忆和域级内存。提出的内存模块存储了目标域的多级表示，该表示域既捕获图像之间的细颗粒差异，又捕获了整体目标域的全局结构。这三个内存模块相互补充，并系统地整合了从底部到向上的多层次监督。三个数据集上的实验表明，多级内存模块合作提高了无监督的跨域重新ID任务，而所提出的MMN可以实现竞争成果。
Unsupervised cross-domain person re-identification (Re-ID) aims to adapt the information from the labelled source domain to an unlabelled target domain. Due to the lack of supervision in the target domain, it is crucial to identify the underlying similarity-and-dissimilarity relationships among the unlabelled samples in the target domain. In order to use the whole data relationships efficiently in mini-batch training, we apply a series of memory modules to maintain an up-to-date representation of the entire dataset. Unlike the simple exemplar memory in previous works, we propose a novel multi-level memory network (MMN) to discover multi-level complementary information in the target domain, relying on three memory modules, i.e., part-level memory, instance-level memory, and domain-level memory. The proposed memory modules store multi-level representations of the target domain, which capture both the fine-grained differences between images and the global structure for the holistic target domain. The three memory modules complement each other and systematically integrate multi-level supervision from bottom to up. Experiments on three datasets demonstrate that the multi-level memory modules cooperatively boost the unsupervised cross-domain Re-ID task, and the proposed MMN achieves competitive results.
