Multimodal-guided self-distillation for unified person search

Person search is challenging due to limitations in identity representation. Existing methods rely on one-hot encoding, ignoring semantic relationships among pedestrians. This leads to a fragmented feature space and reduces generalization ability, especially in large-scale scenarios with a significant proportion of unlabeled identities. For instance, in the CUHK-SYSU dataset, 72.7% of pedestrians lack identity annotations, limiting the effectiveness of supervised learning. To address these issues, we propose a novel Multimodal-Guided Self-Distillation (MGSD) method for Unified Person Search that leverages multimodal textual descriptions and self-distillation to enhance pedestrian representation learning. Specifically, we introduce three key innovations: (1) Multimodal LLM-Assisted Text Generation (MLTG) to provide fine-grained semantic context beyond discrete identity labels, enabling the model to capture inter-person relationships based on clothing attributes, appearance features, and environmental cues; (2) Semantic Structural Consistency Constraint (SSCC) to impose global structural constraints on the feature space, ensuring that distinct identities remain separable while preserving semantic similarities among visually similar individuals; and (3) Multimodal-Aware Self-Distillation Framework (MSDF), where the learnable visual encoder is progressively aligned with the pre-trained CLIP multimodal encoder, improving robustness to variations in illumination, occlusion, and background clutter. Extensive experiments demonstrate that our method significantly enhances retrieval accuracy and generalization, achieving state-of-the-art performance with an mAP of 56.1% on the PRW dataset while maintaining computational efficiency for large-scale real-world applications.