This　paper　introduces　a　lightweight　Semantic-guided　Mutually　Reinforcing　network　(SMR-Net)　for　the　tasks　of　cross-modal　image　fusion　and　salient　object　detection　(SOD).　The　core　concept　of　SMR-Net　is　to　leverage　semantics　for　directing　the　mutual　reinforcing　between　image　fusion　and　SOD.　Specifically,　a　Progressive　Cross-modal　Interaction　(PCI)　image　fusion　subnetwork　is　designed　to　exploit　local　interactions　via　convolution　operations　and　extend　to　global　interactions　utilizing　spatial　and　channel　attention　mechanisms.　Subsequently,　a　cross-modal　Bit-Plane　Slicing-based　SOD　subnetwork　(BPS)　is　developed　by　incorporating　the　fused　image　as　a　third　modality.　This　component　employs　bit-plane　slicing　and　the　deformable　convolution　technique　to　effectively　extract　irregular　semantic　information　embedded　in　fusion　features.　The　refined　semantic　information　then　guides　the　feature　extraction　process　of　the　source　modalities　in　a　reweighted　fashion.　By　cascading　these　two　subnetworks,　BPS　leverages　final　semantic　results　to　direct　PCI　towards　focusing　more　on　semantic　information.　Ultimately,　through　this　semantic-guided　mutual　enhancement　process,　SMR-Net　excels　in　both　producing　high-quality　fused　images　and　achieving　effective　salient　object　detection.　Our　extensive　experiments　on　image　fusion　and　SOD　tasks　convincingly　demonstrate　the　superiority　of　our　network　over　existing　state-of-the-art　alternatives　without　introducing　noticeable　computational　costs.　Compared　to　nearest　competitors,　our　method　demonstrates　a　stronger　generalization　ability　with　26%　fewer　parameters.　Copyright　©　2025,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.