Multi-Masked Prompt Learning For Clothing-Change Person Re-Identification

Clothing-change person re-identification (CC-ReID) aims to match persons even if they change clothes. Thus, extracting clothing-independent features is the key challenge in CC-ReID. Recently, many researches have primarily focused on auxiliary information to realize discriminative feature learning including soft-biometrics features, such as body shape and gaits, and additional clothes labels. However, this method does not fully capture personal information hidden in RGB images. Owing to pretrained vision-language models like CLIP, textual information can describe a person in such a way that it includes all details to capturing personal characteristics, e.g., gender, age, hairstyle, glass, and clothes. Considering that certain personal textual information remains unchanged in CC-ReID, we propose a novel CC-ReID method called Multi-Masked Prompt Learning(MMPL), which takes full advantage of the ability of CLIP to extract clothing-independent features for CC-ReID. MMPL can extract clothing-independent textual and visual features via Prompt Mask and Visual Mask, respectively. This approach enables to discard clothing information from the trained model. The proposed MMPL outperforms other state-of-the-art approaches, improving the rank-1 accuracy to 1.5% and 1.0% on PRCC and LTCC datasets, respectively.