1In　recent　years,　remarkable　advancements　have　been　achieved　in　the　field　of　image　semantic　segmentation　through　the　utilization　of　deep　convolutional　neural　networks　(CNNs).　Nevertheless,　the　development　of　semantic　segmentation　is　hindered　by　the　requirement　of　a　substantial　amount　of　intensively　labeled　training　samples　in　conventional　deep　learning　networks.　The　objective　of　few-shot　semantic　segmentation　is　to　accurately　segment　objects　within　a　target　class　using　a　limited　number　of　annotated　images　for　learning.　Currently,　most　methods　in　the　few-shot　segmentation　field　are　highly　sensitive　to　target　categories,　meaning　that　they　have　weak　generalization　ability　and　that　segmentation　results　can　vary　greatly　across　different　categories.　Furthermore,　a　common　drawback　among　many　of　these　methods　is　the　underutilization　of　the　semantic　information　available　in　the　support　set,　leading　to　suboptimal　segmentation　performance.　To　overcome　these　challenges,　we　introduce　a　novel　strategy　for　encoding　contextual　information　in　our　paper,　specifically　designed　for　few-shot　image　semantic　segmentation.　First,　we　propose　a　metric　network　to　obtain　prototype　representations　of　feature　classes　from　the　supporting　images.　Furthermore,　we　introduce　a　novel　Contextual　Advanced　Semantic　Extraction　(CASE)　module　to　learn　the　trade-off　between　depth,　width,　and　resolution.　To　mitigate　the　detrimental　effects　of　foreground-background　class　imbalance,　we　also　put　forth　a　hybrid　loss　strategy　as　an　additional　contribution.　©　2023　IEEE.