Few-shot　learning　aims　to　recognize　novel　categories　solely　relying　on　a　few　labeled　samples,　with　existing　few-shot　methods　primarily　focusing　on　the　categories　sampled　from　the　same　distribution.　Nevertheless,　this　assumption　cannot　always　be　ensured,　and　the　actual　domain　shift　problem　significantly　reduces　the　performance　of　few-shot　learning.　To　remedy　this　problem,　we　investigate　an　interesting　and　challenging　cross-domain　few-shot　learning　task,　where　the　training　and　testing　tasks　employ　different　domains.　Specifically,　we　propose　aMeta-Memory　scheme　to　bridge　the　domain　gap　between　source　and　target　domains,　leveraging　style-memory　and　content-memory　components.　The　former　stores　intra-domain　style　information　from　source　domain　instances　and　provides　a　richer　feature　distribution.　The　latter　stores　semantic　information　through　exploration　of　knowledge　of　different　categories.　Under　the　contrastive　learning　strategy,　our　model　effectively　alleviates　the　cross-domain　problem　in　few-shot　learning.　Extensive　experiments　demonstrate　that　our　proposed　method　achieves　state-of-the-art　performance　on　cross-domain　few-shot　semantic　segmentation　tasks　on　the　COCO-20(i),　PASCAL-5(i),　FSS-1000,　and　SUIM　datasets　and　positively　affects　few-shot　classification　tasks　on　Meta-Dataset.