Abstract: Few-shot semantic segmentation aims to learn to segment target objects from query images of a given category when only a few samples support image annotation. Recently, matching-based methods establish correlation matching of dense features through 4D convolution and introducing background correlation. However, multiple matching relationships bring more inter-class noise and intra-class noise, and the sparse weights to alleviate the computational pressure of high-dimensional convolution also led to the loss of fine-grained matching accuracy of feature correlation. To alleviate the above problems, A Dual-Feature Guided Hypercorrelation Few-shot Segmentation Network (DFGHNet) is proposed. DFGHNet uses a more efficient 4D convolution kernel to reduce the loss of feature fine-grained matching accuracy caused by weight sparsification. At the same time, it introduces a dual feature mask strategy and adopts a non-local mean feature mapping module without learnable parameters to guide the correlation in the learned matching pattern. In the standard few-shot segmentation benchmarks of PASCAL-5ⁱ and COCO-20ⁱ datasets, the performance of this method achieved the maximum accuracy improvement of 4.8% and 6.5% respectively, verifying the effectiveness of this method.