论文标题
无监督的域自适应人与局部启蒙和原型词典学习
Unsupervised Domain Adaptive Person Re-id with Local-enhance and Prototype Dictionary Learning
论文作者
论文摘要
无监督的域自适应人员重新识别（RE-ID）任务一直是一个挑战，因为与一般域自适应任务不同，人重新ID中的源源数据和目标域数据之间没有重叠，这导致了重要的域差距。最新的无监督重新ID方法使用基于内存的对比损失训练神经网络。但是，通过将每个未标记的实例视为一类，执行对比度学习将导致班级碰撞问题，并且由于在内存库中更新时不同类别的实例数量的差异，更新强度是不一致的。为了解决此类问题，我们提出了针对人重新ID的原型词典学习，它能够通过一个训练阶段同时利用源域数据和目标域数据，同时避免了班级碰撞的问题以及通过集群级原型词典词典学习更新强度不一致的问题。为了减少域间隙在模型上的干扰，我们提出了一个局部增强模块，以改善模型的域适应性，而无需增加模型参数的数量。我们在两个大数据集上的实验证明了原型词典学习的有效性。 71.5 \％的地图是在市场对DUKE任务中实现的，与最新的无监督域自适应重新ID方法相比，这是2.3 \％的改进。它在公爵到市场任务中达到了83.9 \％的地图，与最先进的无监督自适应重新ID方法相比，它的提高了4.4 \％。
The unsupervised domain adaptive person re-identification (re-ID) task has been a challenge because, unlike the general domain adaptive tasks, there is no overlap between the classes of source and target domain data in the person re-ID, which leads to a significant domain gap. State-of-the-art unsupervised re-ID methods train the neural networks using a memory-based contrastive loss. However, performing contrastive learning by treating each unlabeled instance as a class will lead to the problem of class collision, and the updating intensity is inconsistent due to the difference in the number of instances of different categories when updating in the memory bank. To address such problems, we propose Prototype Dictionary Learning for person re-ID which is able to utilize both source domain data and target domain data by one training stage while avoiding the problem of class collision and the problem of updating intensity inconsistency by cluster-level prototype dictionary learning. In order to reduce the interference of domain gap on the model, we propose a local-enhance module to improve the domain adaptation of the model without increasing the number of model parameters. Our experiments on two large datasets demonstrate the effectiveness of the prototype dictionary learning. 71.5\% mAP is achieved in the Market-to-Duke task, which is a 2.3\% improvement compared to the state-of-the-art unsupervised domain adaptive re-ID methods. It achieves 83.9\% mAP in the Duke-to-Market task, which improves by 4.4\% compared to the state-of-the-art unsupervised adaptive re-ID methods.
