This paper presents a new training mechanism called Batch Feature Erasing (BFE) for person re-identification. We apply this strategy to train a novel network with two branches and employing the ResNet-50 as the backbone. The two branches consist of a conventional global branch and a feature erasing branch where the BFE strategy is applied. When training the feature erasing branch, we randomly erase the same region of all the feature maps in a batch. The network then concatenates features from the two branches for person re-identification. Albeit simple, our method achieves state-of-the-art on person re-identification and is applicable to general metric learning tasks in image retrieval problems. For instance, we achieve 75.4% Rank1 accuracy on the CUHK03-Detect dataset and 83.0% Recall-1 score on the Stanford Online Products dataset, outperforming the existed works by a large margin (more than 6%).

本研究介绍了Batch DropBlock (BDB)网络，利用两个分支，卷积神经网络学习全局与局部特征。通过具有随机区域性特殊性和专注性的特征删除模块，构建了局部特征学习的分支，同时保留全局特征学习的信息，实现了更全面和空间分布更均匀的特征表示。最终，方法在行人再识别任务上实现了最优结果，并且其方法同样适用于一般的度量学习任务.

批量DropBlock网络：针对人员重新识别及其它应用的研究