In a conventional domain adaptation person Re-identification (Re-ID) task, both the training and test images in target domain are collected under the sunny weather. However, in reality, the pedestrians to be retrieved may be obtained under severe weather conditions such as hazy, dusty and snowing, etc. This paper proposes a novel Interference Suppression Model (ISM) to deal with the interference caused by the hazy weather in domain adaptation person Re-ID. A teacherstudent model is used in the ISM to distill the interference information at the feature level by reducing the discrepancy between the clear and the hazy intrinsic similarity matrix. Furthermore, in the distribution level, the extra discriminator is introduced to assist the student model make the interference feature distribution more clear. The experimental results show that the proposed method achieves the superior performance on two synthetic datasets than the stateof-the-art methods. The related code will be released online https://github.com/pangjian123/ISM-ReID.

翻译：在常规领域适应人员再识别(Re-ID)任务中,目标领域的培训和测试图像都是在晴天天气下收集的,但在现实中,在严酷的天气条件下,如烟雾、灰尘和下雪等,可以取得要取回的行人。本文件提出一个新的干预禁止干预模式(ISM),以处理干燥天气对域适应人员再识别造成的干扰。IMM使用一个教师模式,通过减少清晰与隐蔽内在相似性矩阵之间的差异,在特征层面提取干扰信息。此外,在分布层面,还引入了额外的区分器,以协助学生模型使干扰特征分布更加明确。实验结果表明,拟议方法在两个合成数据集上取得了优于最新方法的性能。相关代码将在网上发布 https://github.com/pangjian123/ISM-ReID。