Hazy Re-ID: An Interference Suppression Model For Domain Adaptation Person Re-identification Under Inclement Weather Condition
This addresses a practical issue for person re-identification systems where retrieval occurs in severe weather, representing an incremental improvement over existing domain adaptation methods.
The paper tackles the problem of domain adaptation person re-identification under inclement weather conditions by proposing an Interference Suppression Model, which achieves superior performance on two synthetic datasets compared to state-of-the-art methods.
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.