Convolutional　neural　networks　(CNNs)　have　shown　unprecedented　success　in　object　representation　and　detection.　Nevertheless,　CNNs　lack　the　capability　to　model　context　dependencies　among　objects,　which　are　crucial　for　salient　object　detection.　As　the　long　short-term　memory　(LSTM)　is　advantageous　in　propagating　information,　in　this　paper,　we　propose　two　variant　LSTM　units　for　the　exploration　of　contextual　dependencies.　By　incorporating　these　units,　we　present　a　context-aware　network　(CAN)　to　detect　salient　objects　in　RGB-D　images.　The　proposed　model　consists　of　three　components:　feature　extraction,　context　fusion　of　multiple　modalities　and　context-dependent　deconvolution.　The　first　component　is　responsible　for　extracting　hierarchical　features　in　color　and　depth　images　using　CNNs,　respectively.　The　second　component　fuses　high-level　features　by　a　variant　LSTM　to　model　multi-modal　spatial　dependencies　in　contexts.　The　third　component,　embedded　with　another　variant　LSTM,　models　local　hierarchical　context　dependencies　of　the　fused　features　at　multi-scales.　Experimental　results　on　two　public　benchmark　datasets　show　that　the　proposed　CAN　can　achieve　state-of-the-art　performance　for　RGB-D　stereoscopic　salient　object　detection.　©　2020