在行人重识别任务中存在数据集标注难度大，样本量少，特征提取后细节特征缺失等问题。针对以上问题提出深度双重注意力的生成与判别联合学习的行人重识别。首先，构建联合学习框架，将判别模块嵌入生成模块，实现图像生成和判别端到端的训练，及时将生成图像反馈给判别模块，同时优化生成模块与判别模块。其次，通过相邻的通道注意力模块间连接和相邻空间注意力模块间连接，融合所有通道特征和空间特征，构建深度双重注意力模块，将其嵌入教师模型，使模型能更好地提取行人细节身份特征，提高模型识别能力。实验结果表明，该算法在Market-1501和DukeMTMC-ReID数据集上具有较好的鲁棒性、判别性。

In the task of person re-identification, there are problems such as difficulty in labeling datasets, small sample size, and detail feature missing after feature extraction. The joint discriminative and generative learning for person re-identification of the deep dual attention is proposed against the above issues. Firstly, the author constructs a joint learning framework and embeds the discriminative module into the generative module to realize the end-to-end training of image generative and discriminative. Then, the generated pictures are sent to the discriminative module to optimize the generative module and the discriminative module simultaneously. Secondly, according to the connection between the channels of the attention modules and the connection between the attention modules in spaces, it merges all the channel features and spatial features and constructs a deep dual attention module. By embedding the models in the teacher model, the model can better extract the fine-grained features of the objects and improve the recognition ability. The experimental results show that the algorithm has better robustness and discriminative capability on the Market-1501 and the DukeMTMC-ReID datasets.