论文标题
域自适应人员通过耦合优化重新识别
Domain Adaptive Person Re-Identification via Coupling Optimization
论文作者
论文摘要
域自适应人员重新识别（REID）由于域间隙和目标情景的注释短缺而挑战。为了应对这两个挑战，本文提出了一种耦合优化方法，包括域不变映射（DIM）方法和全局本地距离优化（GLO）。与以前在两个阶段转移知识的方法不同，DIM通过将标记和未标记数据集中的图像映射到共享特征空间来实现更有效的一阶段知识传输。 GLO旨在训练REID模型在目标域上使用无监督的设置。 GLO不依赖于为监督培训而设计的现有优化策略，而是涉及更多图像在距离优化方面，并为噪音标签预测提供了更好的鲁棒性。 GLO还将距离优化整合在全球数据集和本地培训批次中，从而表现出更好的训练效率。在三个大尺度数据集（即Market-1501，Dukemtmc-Reid和MSMT17）上进行了广泛的实验表明，我们的耦合优化优化优于最先进的方法。我们的方法在无监督的培训中也很好，甚至胜过最近的几种自适应方法。
Domain adaptive person Re-Identification (ReID) is challenging owing to the domain gap and shortage of annotations on target scenarios. To handle those two challenges, this paper proposes a coupling optimization method including the Domain-Invariant Mapping (DIM) method and the Global-Local distance Optimization (GLO), respectively. Different from previous methods that transfer knowledge in two stages, the DIM achieves a more efficient one-stage knowledge transfer by mapping images in labeled and unlabeled datasets to a shared feature space. GLO is designed to train the ReID model with unsupervised setting on the target domain. Instead of relying on existing optimization strategies designed for supervised training, GLO involves more images in distance optimization, and achieves better robustness to noisy label prediction. GLO also integrates distance optimizations in both the global dataset and local training batch, thus exhibits better training efficiency. Extensive experiments on three large-scale datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, show that our coupling optimization outperforms state-of-the-art methods by a large margin. Our method also works well in unsupervised training, and even outperforms several recent domain adaptive methods.
