SparseInst,　a　recent　lightweight　instance　segmentation　network,　achieves　better　balance　between　efficiency　and　precision.　However,　the　information　contained　in　the　single-layer　features　output　by　the　encoder　is　not　rich　enough　and　the　label　assignment　strategy　leads　to　imbalance　between　positive　and　negative　samples.　In　order　to　further　improve　the　instance　segmentation　performance,　we　propose　LAIS　network　including　a　novel　feature　encoding　module　and　a　Multi-Step　Hungarian　matching　strategy　(MSH).　By　combining　multi-scale　feature　extraction　and　inter-layer　information　fusion,　the　encoder　outputs　features　with　more　detailed　and　comprehensive　information.　By　performing　multiple　rounds　of　one-to-one　Hungarian　matching,　MSH　eliminates　the　imbalance　and　duplication　during　the　sample　allocation.　Experiments　show　that　LAIS　is　more　accurate　than　SparseInst　without　significantly　increasing　parameters　and　GFLOPs.　In　particular,　LAIS　reached　33.8%　AP　on　the　COCO　val,　1.0%　higher　than　SparseInst　when　using　the　same　ResNet-50　Backbone.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.