A big challenge of person re-identification (Re-ID) using a multi-branch network architecture is to learn diverse features from the ID-labeled dataset. The 2-branch Batch DropBlock (BDB) network was recently proposed for achieving diversity between the global branch and the feature-dropping branch. In this paper, we propose to move the dropping operation from the intermediate feature layer towards the input (image dropping). Since it may drop a large portion of input images, this makes the training hard to converge. Hence, we propose a novel double-batch-split co-training approach for remedying this problem. In particular, we show that the feature diversity can be well achieved with the use of multiple dropping branches by setting individual dropping ratio for each branch. Empirical evidence demonstrates that the proposed method performs superior to BDB on popular person Re-ID datasets, including Market-1501, DukeMTMC-reID and CUHK03 and the use of more dropping branches can further boost the performance.

在此研究中，我们提出了一种新的双批次分裂协同训练方法，以解决使用多分支网络架构进行个人再识别（Re-ID）时的训练难度问题，该方法可以通过使用多个特征 dropping 分支以实现特定多样性的学习，并取得了比 Batch DropBlock network 更好的性能。

人物再识别的多样性增强Slow-DropBlock网络