中圖分類號(hào)：TP391文獻(xiàn)標(biāo)識(shí)碼：ADOI: 10.19358/j.issn.2097-1788.2024.02.008
引用格式：及昕浩，彭玉青.基于深度注意力的融合全局和語義特征的圖像描述模型［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2024，43（2）：49-53.

Deep attention based image caption model with fusion of global and semantic feature

Abstract： Aiming at the problems that existing image caption generation models face limitations when utilizing global features due to the fixed receptive field size, and object region based image features lack background information, an image caption model(DFGS) is proposed. A multifeature fusion module is designed to fuse global and semantic feature, allowing the model to focus on key object and background information in the image. A deep attentionbased decoding module is designed to align visual and textual features, enhancing the generation of higher quality image description statements. Experimental results on MSCOCO dataset show that the proposed model can produce more accurate captions, and is competitive compared with other advanced models.

Key words : image caption; global feature; semantic feature; feature fusion