The　effective　modal　fusion　and　perception　between　the　language　and　the　image　are　necessary　for　inferring　the　reference　instance　in　the　referring　image　segmentation　(RIS)　task.　In　this　article,　we　propose　a　novel　RIS　network,　the　global　and　local　interactive　perception　network　(GLIPN),　to　enhance　the　quality　of　modal　fusion　between　the　language　and　the　image　from　the　local　and　global　perspectives.　The　core　of　GLIPN　is　the　global　and　local　interactive　perception　(GLIP)　scheme.　Specifically,　the　GLIP　scheme　contains　the　local　perception　module　(LPM)　and　the　global　perception　module　(GPM).　The　LPM　is　designed　to　enhance　the　local　modal　fusion　by　the　correspondence　between　word　and　image　local　semantics.　The　GPM　is　designed　to　inject　the　global　structured　semantics　of　images　into　the　modal　fusion　process,　which　can　better　guide　the　word　embedding　to　perceive　the　whole　image＇s　global　structure.　Combined　with　the　local-global　context　semantics　fusion,　extensive　experiments　on　several　benchmark　datasets　demonstrate　the　advantage　of　the　proposed　GLIPN　over　most　state-of-the-art　approaches.