  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.
