Zero-shot　learning　(ZSL)　aims　to　recognize　images　of　novel　classes,　but　does　not　use　any　images　belonging　to　the　novel　classes　during　model　training,　which　is　realized　by　exploiting　the　auxiliary　semantic　information.　Recently,　most　ZSL　methods　focus　on　learning　visual-semantic　embeddings　to　transfer　knowledge　from　the　seen　classes　to　the　novel　classes.　Visual-semantic　embedding　is　usually　established　based　on　the　visual　features　of　images　and　the　semantic　information　of　classes,　i.e.,　class　attributes.　However,　image　features　are　extracted　at　the　individual　level,　while　class　attributes　are　obtained　at　the　group　level,　so　the　granularity　of　these　features　is　different,　which　makes　it　difficult　to　match　the　two　kinds　of　features.　To　tackle　such　problem,　we　propose　hierarchical　coupled　discriminative　dictionary　learning　(HCDDL)　method　to　hierarchically　establish　visual-semantic　embedding　at　class-level　and　image-level　with　a　coarse-to-fine　way.　Firstly,　a　class-level　coupled　dictionary　is　trained　to　build　basic　and　coarse-grained　connection　between　visual　space　and　semantic　space.　Using　the　class-level　coupled　dictionary,　image　attributes　are　generated.　Based　on　the　fine-grained　image　attributes　and　images　features,　an　image-level　coupled　dictionary　is　learned.　In　addition,　during　the　learning　of　hierarchical　coupled　dictionaries,　the　discriminative　losses　are　adopted　to　ensure　dictionaries　learn　more　accurate　representation,　which　is　beneficial　to　the　recognition　task.　Recognition　of　unseen　images　is　performed　through　searching　the　class　nearest　to　the　unseen　image　in　multiple　spaces.　Experiments　on　four　widely　used　benchmark　datasets　show　the　effectiveness　of　the　proposed　method,　and　sufficient　ablation　experiments　demonstrate　that　the　coarse-to-fine　way　leads　to　good　performances.　IEEE