In　this　paper,　we　propose　a　Joint　Representation　Attention　Network　(JRA-Net),　an　end-to-end　network,　to　establish　reliable　correspondences　for　image　pairs.　The　initial　correspondences　generated　by　the　local　feature　descriptor　usually　suffer　from　heavy　outliers,　which　makes　the　network　unable　to　learn　a　powerful　enough　representation　for　distinguishing　inliers　and　outliers.　To　this　end,　we　design　a　novel　attention　mechanism.　The　proposed　attention　mechanism　not　only　takes　into　account　the　correlations　between　global　context　and　geometric　information,　but　also　introduces　the　joint　representation　of　different　scales　to　suppress　trivial　correspondences　and　highlight　crucial　correspondences.　In　addition,　to　improve　the　generalization　ability　of　attention　mechanism,　we　present　an　innovative　weight　function,　to　effectively　adjust　the　importance　of　the　attention　mechanism　in　a　learning　manner.　Finally,　by　combining　the　above　components,　the　proposed　JRA-Net　is　able　to　effectively　infer　the　probabilities　of　correspondences　being　inliers.　Empirical　experiments　on　challenging　datasets　demonstrate　the　effectiveness　and　generalization　of　JRA-Net.　We　achieve　remarkable　improvements　compared　with　the　current　state-of-the-art　approaches　on　outlier　rejection　and　relative　pose　estimation.　©　2022　Elsevier　Ltd