Contrastive Language-Image Pre-Training (CLIP) has shown impressive
performance in short-term Person Re-Identification (ReID) due to its ability to
extract high-level semantic features of pedestrians, yet its direct application
to Cloth-Changing Person Re-Identification (CC-ReID) faces challenges due to
CLIP's image encoder overly focusing on clothes clues. To address this, we
propose a novel framework called CLIP-Driven Cloth-Agnostic Feature Learning
(CCAF) for CC-ReID. Accordingly, two modules were custom-designed: the
Invariant Feature Prompting (IFP) and the Clothes Feature Minimization (CFM).
These modules guide the model to extract cloth-agnostic features positively and
attenuate clothes-related features negatively. Specifically, IFP is designed to
extract fine-grained semantic features unrelated to clothes from the raw image,
guided by the cloth-agnostic text prompts. This module first covers the clothes
in the raw image at the pixel level to obtain the shielding image and then
utilizes CLIP's knowledge to generate cloth-agnostic text prompts.
Subsequently, it aligns the raw image-text and the raw image-shielding image in
the feature space, emphasizing discriminative clues related to identity but
unrelated to clothes. Furthermore, CFM is designed to examine and weaken the
image encoder's ability to extract clothes features. It first generates text
prompts corresponding to clothes pixels. Then, guided by these clothes text
prompts, it iteratively examines and disentangles clothes features from
pedestrian features, ultimately retaining inherent discriminative features.
Extensive experiments have demonstrated the effectiveness of the proposed CCAF,
achieving new state-of-the-art performance on several popular CC-ReID
benchmarks without any additional inference time.

提出了一种名为 CLIP-Driven Cloth-Agnostic Feature Learning (CCAF) 的新颖框架，用于解决 Cloth-Changing Person Re-Identification (CC-ReID) 中 Contrastive Language-Image Pre-Training (CLIP) 过度关注衣物线索的问题，在多个流行的 CC-ReID 基准测试上取得了新的最先进性能，而无需额外的推理时间。