%0 Journal Article %A XIONG Junyao %A SONG Zhenfeng %A WANG Rong %T Cross-Modal Target Instance Segmentation Method Based on DMN %D 2022 %R 10.3778/j.issn.1002-8331.2102-0280 %J Computer Engineering and Applications %P 117-123 %V 58 %N 20 %X A cross-modal target instance segmentation method based on DMN, which aims to segment the objects described by natural language expression from the image, is proposed in this paper. First of all, the CBAM attention mechanism is introduced in the visual feature extraction network DPN92, which pays attention to the useful information in space and channel. Secondly, the BN layer is replaced with the normalization of the union of BN and FRN, which reduces the influence batch volume and number of channels in the performance of the extraction characteristic network, and improves the generalization ability of the network. Finally, the proposed scheme is simulated based on three common datasets, ReferIt, GRef and UNC. Simulation results indicate that the mIou evaluation index, which the introduction of CBAM attention mechanism and the joint normalization model, is improved by 1.85 and 0.52 percentage points respectively on the formal two datasets, and is improved by 1.98, 2.22 and 2.75 percentage points on the three validation sets split by UNC, and the improved model is better than the existing model. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2102-0280