Referring　image　segmentation　identifies　the　object　masks　from　images　with　the　guidance　of　input　natural　language　expressions.　Nowadays,　many　remarkable　cross-modal　decoder　are　devoted　to　this　task.　But　there　are　mainly　two　key　challenges　in　these　models.　One　is　that　these　models　usually　lack　to　extract　fine-grained　boundary　information　and　gradient　information　of　images.　The　other　is　that　these　models　usually　lack　to　explore　language　associations　among　image　pixels.　In　this　work,　a　Multi-scale　Gradient　balanced　Central　Difference　Convolution　(MG-CDC)　and　a　Graph　convolutional　network-based　Language　and　Image　Fusion　(GLIF)　for　cross-modal　encoder,　called　Graph-RefSeg,　are　designed.　Specifically,　in　the　shallow　layer　of　the　encoder,　the　MG-CDC　captures　comprehensive　fine-grained　image　features.　It　could　enhance　the　perception　of　target　boundaries　and　provide　effective　guidance　for　deeper　encoding　layers.　In　each　encoder　layer,　the　GLIF　is　used　for　cross-modal　fusion.　It　could　explore　the　correlation　of　every　pixel　and　its　corresponding　language　vectors　by　a　graph　neural　network.　Since　the　encoder　achieves　robust　cross-modal　alignment　and　context　mining,　a　light-weight　decoder　could　be　used　for　segmentation　prediction.　Extensive　experiments　show　that　the　proposed　Graph-RefSeg　outperforms　the　state-of-the-art　methods　on　three　public　datasets.　Code　and　models　will　be　made　publicly　available　at　.　In　this　work,　we　design　a　Multi-scale　Gradient　balanced　Central　Difference　Convolution　(MG-CDC)　and　a　Graph　convolutional　network-based　Language　and　Image　Fusion　(GLIF)　for　cross-modal　encoder.　Since　our　encoder　achieves　robust　cross-modal　alignment　and　context　mining,　we　could　use　a　light-weight　decoder　for　segmentation　prediction.　Extensive　experiments　show　that　our　method　outperforms　the　state-of-the-art　methods　on　three　public　datasets.image