Accurately　locating　the　question-related　regions　in　one　given　image　is　crucial　for　visual　question　answering　(VQA).　The　current　approaches　suffer　two　limitations:　(1)　Dividing　one　image　into　multiple　regions　may　lose　parts　of　semantic　information　and　original　relationships　between　regions;　(2)　Choosing　only　one　or　all　image　regions　to　predict　the　answer　may　correspondingly　result　in　the　insufficiency　or　redundancy　of　information.　Therefore,　how　to　effectively　mine　the　relationship　between　image　regions　and　choose　the　relevant　image　regions　are　vital.　In　this　paper,　we　propose　a　novel　Multi-granularity　feature　interaction　and　Multi-region　selection-based　triplet　VQA　model　(M2TVQA).　To　tackle　the　first　limitation,　we　propose　the　multi-granularity　feature　interaction　strategy　that　adaptively　supplements　the　global　coarse-granularity　features　with　the　regional　fine-granularity　features.　To　overcome　the　second　limitation,　we　design　the　Top-K　learning　strategy　to　adaptively　select　K　most　relevant　image　regions　to　the　question,　even　if　the　selected　regions　are　far　away　in　space.　Such　a　strategy　can　select　as　many　relevant　image　regions　as　possible　and　reduce　introducing　noise.　Finally,　we　construct　the　multi-modality　triplet　to　predict　the　answer　of　VQA.　Extended　experiments　on　two　public　outside　knowledge　datasets　OK-VQA　and　KRVQA　verify　the　effectiveness　of　the　proposed　model.