  In recent years, person re-identification (PReID) has become a hot topic in
computer vision duo to it is an important part in intelligent surveillance.
Many state-of-the-art PReID methods are attention-based or multi-scale feature
learning deep models. However, introducing attention mechanism may lead to some
important feature information losing issue. Besides, most of the multi-scale
models embedding the multi-scale feature learning block into the feature
extraction deep network, which reduces the efficiency of inference network. To
address these issue, in this study, we introduce an attention deep architecture
with multi-scale deep supervision for PReID. Technically, we contribute a
reverse attention block to complement the attention block, and a novel
multi-scale layer with deep supervision operator for training the backbone
network. The proposed block and operator are only used for training, and
discard in test phase. Experiments have been performed on Market-1501,
DukeMTMC-reID and CUHK03 datasets. All the experiment results show that the
proposed model significantly outperforms the other competitive state-of-the-art
methods.
