A recent study published on arXiv addresses the challenge of visible-infrared person re-identification (VI-ReID), focusing on how to effectively connect pedestrian images captured under different lighting conditions. The research emphasizes the inherent differences between visible and infrared images, which complicate the task of matching identities across these modalities. It critiques existing approaches that rely heavily on intermediate representations to align features from the two types of images. By highlighting these limitations, the study contributes to ongoing efforts to improve cross-modality person re-identification systems. This work aligns with other recent research exploring similar challenges in VI-ReID, underscoring the complexity of bridging the gap between visible and infrared image domains. The findings suggest a need for novel representation learning techniques that can better handle modality transitions without depending solely on intermediate feature alignment. Overall, the study advances understanding in the field of computer vision, particularly in applications involving multi-spectral pedestrian recognition.