The　task　of　image-text　retrieval　has　gained　significant　attention　in　the　realm　of　multimodal　artificial　intelligence.　Nonetheless,　existing　works　encounter　challenges　in　efficiently　utilizing　inter-modal　information　and　adequately　leveraging　crucial　intra-modal　details.　In　this　paper,　we　propose　a　novel　common-Memory　Bridged　cross-modal　Adaptive　Graph　Embedding　(MBAGE)　network　for　image-text　retrieval.　Initially,　we　represent　images　and　text　as　graphs,　wherein　nodes　symbolize　salient　regions　and　words.　Subsequently,　we　incorporate　a　common-memory　bank　as　an　intermediate　bridge,　facilitating　interactions　between　nodes　in　the　two　graphs　and　enabling　efficient　utilization　of　inter-modal　information.　Additionally,　we　propose　an　adaptive　graph　convolutional　network　to　implement　intra-modal　interaction,　which　can　adaptively　suppress　the　learning　of　unimportant　nodes.　Finally,　adaptive　pooling　is　employed　to　retain　essential　information,　yielding　a　superior　holistic　embedding.　Experimental　results　on　the　Flickr30K　and　MS-COCO　datasets　demonstrate　that　the　MBAGE　network　not　only　achieves　compelling　retrieval　precision　but　also　exhibits　high　retrieval　efficiency.　©　2024　IEEE.