It　has　become　a　consensus　that　regular　scene　graph　generation　(SGG)　is　limited　in　actual　applications　due　to　the　overfitting　of　head　predicates.　A　series　of　debiasing　methods,　i.e.　unbiased　SGG,　have　been　proposed　to　solve　the　problem.　However,　existing　unbiased　SGG　methods　have　a　tendency　to　fit　the　tail　predicates,　which　is　another　type　of　bias.　This　paper　aims　to　eliminate　the　one-way　overfitting　of　head　or　tail　predicates.　In　order　to　provide　more　balanced　relationship　prediction,　we　propose　a　new　framework　DCL　(Dual-branch　Cumulative　Learning)　which　integrates　regular　relation　prediction　process　and　debiasing　relation　prediction　process　by　employing　cumulative　learning　mechanism.　The　learning　process　of　DCL　enhances　the　discrimination　of　tail　predicates　without　reducing　the　discrimination　performance　of　the　model　on　head　predicates.　DCL　is　model-agnostic　and　compatible　with　existed　different　type　of　debiasing　methods.　Experiments　on　Visual　Genome　dataset　show　that,　among　all　the　model-agnostic　methods,　DCL　achieves　the　best　comprehensive　performance　while　considering　both　R@K　and　mR@K.