  Occlusion is still a severe problem in the video-based Re-IDentification
(Re-ID) task, which has a great impact on the success rate. The attention
mechanism has been proved to be helpful in solving the occlusion problem by a
large number of existing methods. However, their attention mechanisms still
lack the capability to extract sufficient discriminative information into the
final representations from the videos. The single attention module scheme
employed by existing methods cannot exploit multi-scale spatial cues, and the
attention of the single module will be dispersed by multiple salient parts of
the person. In this paper, we propose a Concentrated Multi-grained
Multi-Attention Network (CMMANet) where two multi-attention modules are
designed to extract multi-grained information through processing multi-scale
intermediate features. Furthermore, multiple attention submodules in each
multi-attention module can automatically discover multiple discriminative
regions of the video frames. To achieve this goal, we introduce a diversity
loss to diversify the submodules in each multi-attention module, and a
concentration loss to integrate their attention responses so that each
submodule can strongly focus on a specific meaningful part. The experimental
results show that the proposed approach outperforms the state-of-the-art
methods by large margins on multiple public datasets.
