Few-shot　semantic　segmentation　intends　to　predict　pixel-level　categories　using　only　a　few　labeled　samples.　Existing　few-shot　methods　focus　primarily　on　the　categories　sampled　from　the　same　distribution.　Nevertheless,　this　assumption　cannot　always　be　ensured.　The　actual　domain　shift　problem　significantly　reduces　the　performance　of　few-shot　learning.　To　remedy　this　problem,　we　propose　an　interesting　and　challenging　cross-domain　few-shot　semantic　segmentation　task,　where　the　training　and　test　tasks　perform　on　different　domains.　Specifically,　we　first　propose　a　meta-memory　bank　to　improve　the　generalization　of　the　segmentation　network　by　bridging　the　domain　gap　between　source　and　target　domains.　The　meta-memory　stores　the　intra-domain　style　information　from　source　domain　instances　and　transfers　it　to　target　samples.　Subsequently,　we　adopt　a　new　contrastive　learning　strategy　to　explore　the　knowledge　of　different　categories　during　the　training　stage.　The　negative　and　positive　pairs　are　obtained　from　the　proposed　memory-based　style　augmentation.　Comprehensive　experiments　demonstrate　that　our　proposed　method　achieves　promising　results　on　cross-domain　few-shot　semantic　segmentation　tasks　on　C000-20(i)　,　PASCAL-5(i),　FSS-1000,　and　SHIM　datasets.