The paper titled "ProFD: Prompt-Guided Feature Disentangling for Occluded Person Re-Identification" addresses the challenges associated with occlusion in person re-identification (ReID) tasks. Traditional methods often struggle to accurately extract features of human body parts due to occlusion and the noise introduced by external spatial information. This results in misaligned part features, which can significantly hinder the performance of ReID systems. To overcome these issues, the authors propose a novel approach called Prompt-guided Feature Disentangling (ProFD). This method utilizes the rich knowledge embedded in pre-trained textual models to enhance the alignment of visual features with textual prompts. ProFD begins by designing part-specific prompts and employs noisy segmentation masks to align visual and textual embeddings, allowing the model to gain spatial awareness of the prompts. To further mitigate the impact of noise from external masks, ProFD incorporates a hybrid-attention decoder. This component ensures that both spatial and semantic consistency are maintained during the decoding process, which helps to reduce the noise's influence on the feature extraction. Additionally, to prevent catastrophic forgetting and overfitting, the authors implement a self-distillation strategy that retains the pre-trained knowledge from the CLIP model. The effectiveness of ProFD is demonstrated through evaluations on several benchmark datasets, including Market1501, DukeMTMC-ReID, Occluded-Duke, Occluded-ReID, and P-DukeMTMC. The results indicate that ProFD achieves state-of-the-art performance in the field of occluded person re-identification. The paper has been accepted for presentation at the ACM Multimedia Conference in 2024, highlighting its significance and contribution to the ongoing research in computer vision and pattern recognition. The authors, Can Cui, Siteng Huang, Wenxuan Song, Pengxiang Ding, Min Zhang, and Donglin Wang, have made their project available for further exploration and use by the research community.