  Cloth-changing person re-identification (CC-ReID) aims to match persons who
change clothes over long periods. The key challenge in CC-ReID is to extract
clothing-independent features, such as face, hairstyle, body shape, and gait.
Current research mainly focuses on modeling body shape using multi-modal
biological features (such as silhouettes and sketches). However, it does not
fully leverage the personal description information hidden in the original RGB
image. Considering that there are certain attribute descriptions which remain
unchanged after the changing of cloth, we propose a Masked Attribute
Description Embedding (MADE) method that unifies personal visual appearance and
attribute description for CC-ReID. Specifically, handling variable
clothing-sensitive information, such as color and type, is challenging for
effective modeling. To address this, we mask the clothing and color information
in the personal attribute description extracted through an attribute detection
model. The masked attribute description is then connected and embedded into
Transformer blocks at various levels, fusing it with the low-level to
high-level features of the image. This approach compels the model to discard
clothing information. Experiments are conducted on several CC-ReID benchmarks,
including PRCC, LTCC, Celeb-reID-light, and LaST. Results demonstrate that MADE
effectively utilizes attribute description, enhancing cloth-changing person
re-identification performance, and compares favorably with state-of-the-art
methods. The code is available at https://github.com/moon-wh/MADE.
