Person re-identification based on two-layer attention and joint learning

To solve the problems of intra-class difference and modal difference in cross-modality person re-identification tasks, person re-identification based on two-layer attention and joint learning is proposed. Firstly, the input visible images are enhanced by gray-like channels for joint learning. Then, to obtain more effective feature semantics in space and channels, a two-layer attention module is embedded behind the third convolutional layer of Resnet50. Finally, to promote network convergence and reduce the difference between different modalities, a modality distribution difference loss function is utilized. The effectiveness of this algorithm has been experimentally proved not only in two mainstream public datasets SYSU-MM01 and RegDB, but also in the newly proposed dataset LLCM. The effectiveness of these three modules is proved by ablation experiments on the SYSU-MM01 dataset.