关键词: 图像描述, 深度学习, 注意力机制, 多模态, 自然语言处理

Abstract: For that the image caption model under deep learning framework has the problem of inaccurate selection and insufficient utilization of image features，which leads to the low quality of generated image caption statements，an image caption model based on attention feature adaptive recalibration was proposed. Firstly，the convolutional neural network was utilized to extract image features，and the attention mechanism was integrated in the image features to obtain the attention features with location information，so that the corresponding image regions were dynamically focused when the words were output in order. Then，a channel activation layer was constructed to fully capture channel-wise dependencies for attention feature adaptive recalibration，which boosted the representational power of the features，and ultimately improved the quality of generated sentences by Long Short-Term Memory（LSTM）network. A comparison experiment was conducted on the three standard data sets of MS COCO，Flickr8K and Flickr30K. The experiment results show that the scores of BLEU_1，BLEU_2， BLEU_3，BLEU_4，Meteor and CIDEr of the proposed model on MS COCO data set can achieve 69. 4%，52. 3%，38. 6%， 28. 5%，23. 3% and 83. 6%，which are superior to the traditional neural network image caption model. The proposed model can generate more accurate image caption.

Key words: image caption, deep learning, attention mechanism, multimodal, Natural Language Processing (NLP)