To　address　the　issue　that　the　contextual　information　obtained　by　existing　scene　graph　generation　methods　is　limited,　an　effective　context　fusion　module　was　proposed,　which　is　the　dual-stream　multi-head　attention　module　(DMA).　By　using　DMA　for　object　classification　and　relationship　classification,　the　dual-stream　multi-head　attention-based　scene　graph　generation　network　(DMA-Net)　was　suggested.　The　proposed　method　consists　of　object　detection,　object　semantic　parsing,　and　relationship　semantic　parsing.　First,　the　object　detection　module　located　the　objects　in　the　image　and　extracted　the　features　of　the　objects.　Second,　the　object　dual-stream　multi-head　attention　(O-DMA)　in　object　semantic　parsing　module　was　used　to　obtain　the　features　fused　with　node　contexts,　which　were　decoded　by　the　object　semantic　decoder　to　obtain　the　object　labels.　Finally,　the　features　fused　with　edge　contexts　were　output　by　the　relationship　dual-stream　multi-head　attention　(R-DMA)　in　relationship　semantic　parsing　module　and　decoded　by　the　relationship　semantic　decoder　to　get　the　relationship　labels.　Comparisons　with　the　proposed　method　and　mainstream　scene　graph　generation　methods　were　conducted　on　the　publicly　available　visual　genome　(VG)　dataset,　the　graph　constraint　recall　and　no　graph　constraint　recall　of　DMA-Net　for　three　subtasks　including　scene　graph　detection,　scene　graph　classification,　and　predicate　classification　were　computed　for　each　method.　Results　show　that　the　proposed　method　can　fully　exploit　the　contextual　information　in　the　scene,　which　enhances　the　representation　capability　of　features　and　improves　the　accuracy　of　the　scene　graph　generation　task.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.