Text-Based Person re-ID by Saliency Mask and Dynamic Label Smoothing

The current text-based person re-identification (re-ID) models tend to learn salient features of image and text, which however is prone to failure in identifying persons with very similar dress, because their image contents with observable but indescribable difference may have identical textual description. To address this problem, we propose a saliency mask based re-ID model to learn non-salient but highly discriminative features, which can work together with the salient features to provide more robust pedestrian identification. To further improve the performance of the model, a dynamic label smoothing based cross-modal projection matching loss (named CMPM-DS) is proposed to train our model, and our CMPM-DS can adaptively adjust the smoothing degree of the true distribution. We conduct extensive ablation and comparison experiments on two popular re-ID benchmarks to demonstrate the efficiency of our model and loss function, and improving the existing best R@1 by 0.33% on CUHK-PEDE and 4.45% on RSTPReID.