  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.
