论文标题
注释有效的人重新识别，基于聚类的对选择
Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection
论文作者
论文摘要
人重新识别（RE-ID）由于其有前途的现实应用而引起了极大的关注。但是，实际上，注释培训数据以培训重新ID模型总是很昂贵的，并且在维持重新ID任务的同时降低注释成本的同时，降低注释成本仍然具有挑战性。为了解决这个问题，我们提出了有效的人重新识别方法，以根据对的谬误和多样性从设置的替代对中选择图像对，并根据注释训练重新ID模型。具体而言，我们设计一个注释和训练框架，首先通过考虑特征局部的所有图像来减少设置的替代对的大小，其次选择图像对人体内/群群的样本对人类进行注释，以注释，第三，第三重新分配簇，并根据注释训练模型，并最终与重新组装的Clusters一起训练模型。 During the pair selection, we seek for valuable pairs according to pairs' fallibility and diversity, which includes an intra-cluster criterion to construct image pairs with the most chaotic samples and the representative samples within clusters, an inter-cluster criterion to construct image pairs between clusters based on the second-order Wasserstein distance, and a diversity criterion for clusterbased pair selection.结合了上面的所有标准，制定了一种贪婪的策略来解决配对选择问题。最后，将重复上述群集选择 - 避开培训程序，直到达到注释预算为止。对三个广泛采用的重新ID数据集进行了广泛的实验表明，与最先进的作品相比，我们可以大大降低注释成本，同时实现更好的性能。
Person Re-identification (Re-ID) has attracted great attention due to its promising real-world applications. However, in practice, it is always costly to annotate the training data to train a Re-ID model, and it still remains challenging to reduce the annotation cost while maintaining the performance for the Re-ID task. To solve this problem, we propose the Annotation Efficient Person Re-Identification method to select image pairs from an alternative pair set according to the fallibility and diversity of pairs, and train the Re-ID model based on the annotation. Specifically, we design an annotation and training framework to firstly reduce the size of the alternative pair set by clustering all images considering the locality of features, secondly select images pairs from intra-/inter-cluster samples for human to annotate, thirdly re-assign clusters according to the annotation, and finally train the model with the re-assigned clusters. During the pair selection, we seek for valuable pairs according to pairs' fallibility and diversity, which includes an intra-cluster criterion to construct image pairs with the most chaotic samples and the representative samples within clusters, an inter-cluster criterion to construct image pairs between clusters based on the second-order Wasserstein distance, and a diversity criterion for clusterbased pair selection. Combining all criteria above, a greedy strategy is developed to solve the pair selection problem. Finally, the above clustering-selecting-annotating-reassigning-training procedure will be repeated until the annotation budget is reached. Extensive experiments on three widely adopted Re-ID datasets show that we can greatly reduce the annotation cost while achieving better performance compared with state-of-the-art works.
