For　the　instance　segmentation　task,　instance　representation　directly　determines　the　quality　of　generated　masks,　so　achieving　efficient　and　accurate　instance　representation　is　crucial.　Grid-based　or　box-based　instance　representation　contains　redundant　information　from　background　or　other　instances,　activation-based　instance　representation　includes　a　small　part　of　the　instance.　The　instance　representation　based　on　current　methods　is　not　accurate　enough.　In　order　to　represent　more　information　of　an　instance　under　the　condition　of　excluding　irrelevant　information,　this　paper　proposes　multi-area　representation　(MAR),　which　is　in　the　form　of　a　scale-adaptive　multi-area　activation　map　generated　by　a　multi-branch　structure.　MAR　can　adapt　to　the　structure　and　pose　of　an　instance,　thereby　representing　the　shape　and　size　of　the　instance.　Experiments　show　that,　compared　with　SparseInst,　MARInst　can　improve　the　performance　of　instance　segmentation　and　keep　the　inference　speed　and　training　memory　almost　unchanged.　In　particular,　MARInst　achieved　30.3%　AP　on　the　MS　COCO　2017　val,　and　1.6%　AP　higher　than　SparseInst　when　using　the　same　ResNet-50　backbone,　proving　the　effectiveness　of　the　proposed　method.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2023.