An image description model based on improved attention mechanism feature fusion

Image description refers to the process of automatically generating natural language descriptions that are strongly related to the content of an image, and it is a cross-disciplinary field that combines computer vision and natural language processing. This paper proposes an improved attention mechanism for image feature fusion, which addresses the limitations of existing image feature extraction methods. The proposed method uses an encoder-decoder structure, where an improved GAM attention module is used to fuse the grid features of images with edge features. Furthermore, a mesh memory structure is employed to further enhance the fused features, resulting in richer image features. Through the decoder, more accurate image descriptions can be generated. The model was evaluated using mainstream evaluation metrics BLEU, ROUGEL, CIDEr, and METEOR, and validated on the public MS COCO dataset. Experimental outcomes demonstrate that the image description model proposed based on GAM, which merges different features, achieves favorable performance across various evaluation criteria and further enhances the capability of image description.