Several recent person re-identification methods are focusing on learning discriminative representations by designing efficient metric learning loss functions. Other approaches design part based architectures to compute an informative descriptor based on local features from semantically coherent parts. Few efforts learn the relationship between distant similar regions and parts by adjusting them to their most feasible positions with the help of soft attention. However, they focus on calibrating distant similar parts features and ignore to learn the noise (blur) free and distinct feature representations as the person re-identification datasets contain degraded images. To tackle these issues, we propose a novel Consistent Attention Dual Branch Network (CadNet) that has ability to model long-range dependencies (correlations) between channels as well as feature maps. We adopt multiple classifiers trained to learn the most discriminative global features for a unique representation of a person. Correlation between channels are consistently computed by using channel attention mechanism to make the learned feature noise free and distict from noisy and blurry data. Feature correlations interpret the relationship between distant similarities in the images computed by the self attention mechanism. The proposed CadNet significantly enhances the performance with respect to the baseline on the person re-identification benchmarks
