关键词: 图像标注生成, 图像内容描述, 深度神经网络, 视觉注意力, 语义信息

Abstract: Aiming at the problem that the current image semantic generation model does not adequately describe the details of the object in the images, this paper proposes an image content description structure which combines the dynamic semantic guidance of image and the adaptive attention mechanism. In the model, according to the last-time prediction word, the attention mechanism adaptively chooses the image part which will be processed in the next-time. In addition, the model builds dense image information as the additional monitoring information, so that makes the model description image associating the image semantic information with the attention information. The training and testing are done in Flickr8K and Flickr30K databases, the experimental results using different evaluations show that the proposed model has good performance. Especially, compared with Guiding-Long Short-Term Memory model, the score increases 4.1, 1.8, 2.4, 0.8, 3.1, up to 6.3%，4.0%，7.9%，3.9%，17.3%; Compared with Soft-Attention, the score improves 1.9, 2.4, 3.3, 1.5, 2.74, up to 2.8%, 5.5%, 11.1%, 7.5%, 14.8%.

Key words: image caption generation, image description, deep neural networks, visual attention mechanism, semantic information