Recent　deep　learning　models　have　advanced　object　detection　capabilities,　but　their　reliance　on　labeled　data　limits　scalability.　Fine-grained　zero-shot　detection　remains　challenging　due　to　the　need　to　generalize　to　unseen　classes　and　distinguish　subtle　features.　To　address　this,　we　propose　Fine-Grained　Zero-Shot　Object　Detection　via　Semantic　Decoupling　(ZSD-SD).　Our　approach　introduces　a　semantic　decoupling　module　to　separate　visual　features　into　semantic-related　and　semantic-unrelated　components,　facilitating　alignment　between　visual　and　semantic　spaces.　A　semantic-visual　learning　module　further　decouples　these　features,　enhancing　visual　representations　relevant　to　external　semantic　knowledge.　This　semantic-related　representation　significantly　improves　zero-shot　performance.　Experiments　on　two　fine-grained　object　detection　datasets　demonstrate　that　ZSD-SD　achieves　state-of-the-art　results.　©　2024　Copyright　held　by　the　owner/author(s).