The field of person re-identification is witnessing significant developments, with a focus on improving the accuracy and robustness of models in various scenarios. Researchers are exploring new approaches to address challenging issues such as domain gaps, hard samples, and cross-modal recognition. The use of deep learning techniques, such as Siamese networks and multimodal-guided learning, is becoming increasingly popular. Additionally, there is a growing interest in developing methods that can effectively utilize weak supervision and mitigate the need for large amounts of labeled data. These advancements have the potential to enhance the performance of person re-identification models in real-world applications. Notable papers in this area include: Minimizing the Pretraining Gap: Domain-aligned Text-Based Person Retrieval, which introduces a unified pipeline for domain adaptation at both image and region levels, and Try Harder: Hard Sample Generation and Learning for Clothes-Changing Person Re-ID, which proposes a novel multimodal-guided framework for generating and optimizing hard samples.