In　this　paper,　we　propose　a　novel　visual　Semantic-Spatial　Self-Highlighting　Network　(termed　3SHNet)　for　high-precision,　high-efficiency　and　high-generalization　image–sentence　retrieval.　3SHNet　highlights　the　salient　identification　of　prominent　objects　and　their　spatial　locations　within　the　visual　modality,　thus　allowing　the　integration　of　visual　semantics–spatial　interactions　and　maintaining　independence　between　two　modalities.　This　integration　effectively　combines　object　regions　with　the　corresponding　semantic　and　position　layouts　derived　from　segmentation　to　enhance　the　visual　representation.　And　the　modality-independence　guarantees　efficiency　and　generalization.　Additionally,　3SHNet　utilizes　the　structured　contextual　visual　scene　information　from　segmentation　to　conduct　the　local　(region-based)　or　global　(grid-based)　guidance　and　achieve　accurate　hybrid-level　retrieval.　Extensive　experiments　conducted　on　MS-COCO　and　Flickr30K　benchmarks　substantiate　the　superior　performances,　inference　efficiency　and　generalization　of　the　proposed　3SHNet　when　juxtaposed　with　contemporary　state-of-the-art　methodologies.　Specifically,　on　the　larger　MS-COCO　5K　test　set,　we　achieve　16.3%,　24.8%,　and　18.3%　improvements　in　terms　of　rSum　score,　respectively,　compared　with　the　state-of-the-art　methods　using　different　image　representations,　while　maintaining　optimal　retrieval　efficiency.　Moreover,　our　performance　on　cross-dataset　generalization　improves　by　18.6%.　©　2024