What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification.

•A novel deep architecture to emphasize common local patterns is proposed to learn flexible joint representations for person re-identification.•The proposed method introduces a multiplicative integration gating function to embed two convolutional features to their joint representations, which are effective in discriminating positive pairs from negative pairs.•Spatial dependencies are incorporated into feature learning to address the cross-view misalignment.•Extensive experiments and empirical analysis are provided in experimental part.