The　quality　of　underwater　imaging　is　severely　compromised　due　to　the　light　scattering　and　absorption　caused　by　suspended　particles,　limiting　the　effectiveness　of　following　underwater　salient　object　detection　(USOD)　tasks.　Polarization　information　offers　a　unique　perspective　by　interpreting　the　intrinsic　physical　properties　of　objects,　potentially　enhancing　the　contrast　between　objects　and　background　in　complex　scenes.　However,　it　is　rarely　applied　in　the　field　of　USOD.　In　this　paper,　we　build　a　dataset　named　TJUP-USOD,　which　includes　both　RGB　and　polarization　(i,e.,　RGB-P)　images;　based　on　this,　we　design　a　USOD　network,　called　STAMF,　to　explore　the　strengths　of　both　color　and　polarization　information.　STAMF　synthesizes　these　complementary　information　streams　to　generate　high-contrast,　vivid　scene　representations　that　improve　the　discernibility　of　underwater　features.　Specifically,　the　Omnidirectional　Tokens-to-Token　Vision　Mamba　notably　amplifies　the　capacity　to　handle　both　global　and　local　information　by　employing　multidirectional　scanning　and　iterative　integration　of　inputs.　Besides,　introducing　the　Mamba　Cross-Modal　Fusion　Module　adeptly　merges　RGB　and　polarization　features,　amalgamating　global　insights　to　refine　local　pixel-wise　fusion　precision　and　alleviate　overall　misguidance　resulting　from　the　fusion　of　erroneous　modal　data　in　demanding　underwater　environments.　Comparative　experiments　with　27　methods　and　extensive　ablation　study　results　demonstrate　that,　the　proposed　STAMF,　with　only　25.85　million　parameters,　effectively　leverages　RGB-P　information,　achieving　state-of-the-art　performance,　and　opens　a　new　door　for　the　USOD　tasks.　The　proposed　STAMF　once　again　demonstrates　the　importance　of　increasing　the　dimensionality　of　the　dataset　for　USOD;　and　further　exploring　the　advantages　of　network　structures　based　on　multi-dimensional　data　will　further　enhance　task　performance.　The　code　and　dataset　are　publicly　available:　https://github.com/Kingwin97/STAMF.　©　2025　Elsevier　B.V.