Dynamic Weighted Gradient Reversal Network for Visible-infrared Person Re-identification

Abstract

Due to intra-modality variations and cross-modality discrepancy, visible-infrared person re-identification (VI Re-ID) is an important and challenging task in intelligent video surveillance. The cross-modality discrepancy is mainly caused by the differences between visible images and infrared images, the inherent essence of which is heterogeneous. To alleviate this discrepancy, we propose a Dynamic Weighted Gradient Reversal Network (DGRNet) to enhance the learning of discriminative common representations by confusing the modality discrimination. In the proposed DGRNet, we design the gradient reversal model guiding adversarial training between identity classifier and modality discriminator to reduce the modality discrepancy of the same person in different modalities. Furthermore, we propose an optimization training method, that is, designing dynamic weight of gradient reversal to achieve optimal adversarial training, and dynamic weight has the ability to dynamically and adaptively evaluate the significance of target loss term, without involving hyper-parameter tuning. Extensive experiments were conducted on two public VI Re-ID datasets, SYSU-MM01 and RegDB. The experimental results show that the proposed DGRNet outperforms state-of-the-art methods and demonstrate the effectiveness of the DGRNet to learn more discriminative common representations for VI Re-ID.