Text-based　person　search　aims　to　use　text　descriptions　to　search　for　corresponding　person　images.　However,　due　to　the　obvious　pattern　differences　in　image　and　text　modalities,　it　is　still　a　challenging　problem　to　align　the　two　modalities.　Most　existing　approaches　only　consider　semantic　alignment　within　a　global　context　or　partial　parts,　lacking　consideration　of　how　to　match　image　and　text　in　terms　of　differences　in　model　information.　Therefore,　in　this　paper,　we　propose　an　efficient　Modality-Aligned　Person　Search　network　(MAPS)　to　address　this　problem.　First,　we　suppress　image-specific　information　by　image　feature　style　normalization　to　achieve　modality　knowledge　alignment　and　reduce　information　differences　between　text　and　image.　Second,　we　design　a　multi-granularity　modal　feature　fusion　and　optimization　method　to　enrich　the　modal　features.　To　address　the　problem　of　useless　and　redundant　information　in　the　multi-granularity　fused　features,　we　propose　a　Multi-granularity　Feature　Self-optimization　Module　(MFSM)　to　adaptively　adjust　the　corresponding　contributions　of　different　granularities　in　the　fused　features　of　the　two　modalities.　Finally,　to　address　the　problem　of　information　inconsistency　in　the　training　and　inference　stages,　we　propose　a　Cross-instance　Feature　Alignment　(CFA)　to　help　the　network　enhance　category-level　generalization　ability　and　improve　retrieval　performance.　Extensive　experiments　demonstrate　that　our　MAPS　achieves　state-of-the-art　performance　on　all　text-based　person　search　datasets,　and　significantly　outperforms　other　existing　methods.　©　2024