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Person Re-Identification (Re-ID) has witnessed great advance, driven by thedevelopment of deep learning. However, modern person Re-ID is still challengedby background clutter, occlusion and large posture variation which are commonin practice. Previous methods tackle these challenges by localizing pedestriansthrough external cues (e.g., pose estimation, human parsing) or attentionmechanism, suffering from high computation cost and increased model complexity.In this paper, we propose the Contextual Mutual Boosting Network (CMBN). Itlocalizes pedestrians and recalibrates features by effectively exploitingcontextual information and statistical inference. Firstly, we construct twobranches with a shared convolutional frontend to learn the foreground andbackground features respectively. By enabling interaction between these twobranches, they boost the accuracy of the spatial localization mutually.Secondly, starting from a statistical perspective, we propose the MaskGenerator that exploits the activation distribution of the transformationmatrix for generating the static channel mask to the representations. The maskrecalibrates the features to amplify the valuable characteristics and diminishthe noise. Finally, we propose the Contextual-Detachment Strategy to optimizethe two branches jointly and independently, which further enhances thelocalization precision. Experiments on the benchmarks demonstrate thesuperiority of the architecture compared the state-of-the-art.

Robust Person Re-Identification through Contextual Mutual Boosting