The　key　challenge　of　cross-modal　salient　object　detection　lies　in　the　representational　discrepancy　between　different　modal　inputs.　Existing　methods　typically　employ　only　one　encoding　mode,　either　constrained　encoding　to　extract　modality-shared　characteristics,　or　unconstrained　encoding　to　capture　modality-specific　traits.　However,　the　use　of　a　single　paradigm　limits　the　capability　of　capturing　salient　cues,　thus　leading　to　poor　generalization　of　existing　methods.　We　propose　a　novel　learning　paradigm　named　＂Collaborating　Constrained　and　Unconstrained　Encodings＂　(CCUE)　that　integrates　constrained　and　unconstrained　feature　extraction　to　discover　richer　salient　cues.　Accordingly,　we　establish　a　CCUE　network　(CCUENet)　consisting　of　a　constrained　branch　and　an　unconstrained　branch.　The　representations　at　each　level　from　these　two　branches　are　integrated　in　an　Information　Selection　and　Fusion　(ISF)　module.　The　novelty　of　this　module　lies　in　its　selective　fusion　of　the　important　information　from　each　feature　primarily　based　on　the　response　degree,　which　enables　the　network　to　aggregate　effective　cues　for　saliency　detection.　In　the　network　training　stage,　we　propose　a　Multi-scale　Boundary　Information　(MBI)　loss,　which　can　constrain　the　detection　results　to　retain　clear　region　boundaries　and　boost　the　model＇s　robustness　to　variations　in　object　scale.　Under　the　supervision　of　MBI　loss,　CCUENet　is　able　to　output　high-quality　saliency　maps.　The　experimental　results　show　that　CCUENet　exhibits　superior　performance　on　RGB-T　and　RGB-D　datasets.