  Person Re-Identification (Re-ID) has witnessed great advance, driven by the
development of deep learning. However, modern person Re-ID is still challenged
by background clutter, occlusion and large posture variation which are common
in practice. Previous methods tackle these challenges by localizing pedestrians
through external cues (e.g., pose estimation, human parsing) or attention
mechanism, suffering from high computation cost and increased model complexity.
In this paper, we propose the Contextual Mutual Boosting Network (CMBN). It
localizes pedestrians and recalibrates features by effectively exploiting
contextual information and statistical inference. Firstly, we construct two
branches with a shared convolutional frontend to learn the foreground and
background features respectively. By enabling interaction between these two
branches, they boost the accuracy of the spatial localization mutually.
Secondly, starting from a statistical perspective, we propose the Mask
Generator that exploits the activation distribution of the transformation
matrix for generating the static channel mask to the representations. The mask
recalibrates the features to amplify the valuable characteristics and diminish
the noise. Finally, we propose the Contextual-Detachment Strategy to optimize
the two branches jointly and independently, which further enhances the
localization precision. Experiments on the benchmarks demonstrate the
superiority of the architecture compared the state-of-the-art.
