Accurately　identifying　correct　correspondences　(inliers)　in　two-view　images　is　a　fundamental　task　in　computer　vision.　Recent　studies　usually　adopt　Graph　Neural　Networks　or　stack　local　graphs　into　global　ones　to　establish　neighborhood　relations.　However,　the　smoothing　properties　of　Graph　Convolutional　Neural　network　(GCN)　cause　the　model　to　fall　into　local　extreme,　which　leads　to　the　issue　of　indistinguishability　between　inliers　and　outliers.　Especially　when　the　initial　correspondences　contain　a　large　number　of　incorrect　correspondences　(outliers),　these　studies　suffer　from　severe　performance　degradation.　To　address　the　above　issues　and　refocus　perspective　information　on　distinct　features,　we　design　a　Consistency　Guided　ResFormer　Network　(CGR-Net)　that　uses　consistent　correspondences　to　guide　model　perspective　focusing,　thereby　avoiding　the　negative　impact　of　outliers.　Specifically,　we　design　an　efficient　Graph　Score　Calculation　module,　which　aims　to　compute　global　graph　scores　by　enhancing　the　representation　of　important　features　and　comprehensively　capturing　the　contextual　relationships　between　correspondences.　Then,　we　propose　a　Consistency　Guided　Correspondences　Selection　module　to　dynamically　fuse　global　graph　scores　and　consistency　graphs　and　construct　a　novel　consistency　matrix　to　accurately　recognize　inliers.　Extensive　experiments　on　various　challenging　tasks　demonstrate　that　our　CGR-Net　outperforms　state-of-the-art　methods.　Our　code　is　released　at　https://github.com/XiaojieLi11/CGR-Net.