Many　convolutional　neural　network　(CNN)-based　approaches　for　stereoscopic　salient　object　detection　involve　fusing　either　low-level　or　high-level　features　from　the　color　and　disparity　channels.　The　former　method　generally　produces　incomplete　objects,　whereas　the　latter　tends　to　blur　object　boundaries.　In　this　paper,　a　coupled　CNN　(CoCNN)　is　proposed　to　fuse　color　and　disparity　features　from　low　to　high　layers　in　a　unified　deep　model.　It　consists　of　three　parts:　two　parallel　multilinear　span　networks,　a　cascaded　span　network　and　a　conditional　random　field　module.　We　first　apply　the　multilinear　span　network　to　compute　multiscale　saliency　predictions　based　on　RGB　and　disparity　individually.　Each　prediction,　learned　under　separate　supervision,　utilizes　the　multilevel　features　extracted　by　the　multilinear　span　network.　Second,　a　proposed　cascaded　span　network,　under　deep　supervision,　is　designed　as　a　coupling　unit　to　fuse　the　two　feature　streams　at　each　scale　and　integrate　all　fused　features　in　a　supervised　manner　to　construct　a　saliency　map.　Finally,　we　formulate　a　constraint　in　the　form　of　a　conditional　random　field　model　to　refine　the　saliency　map　based　on　the　a　priori　assumption　that　objects　with　similar　saliency　values　have　similar　colors　and　disparities.　Experiments　conducted　on　two　commonly　used　datasets　demonstrate　that　the　proposed　method　outperforms　previous　state-of-the-art　methods.　(C)　2020　Published　by　Elsevier　Ltd.