基于卷积块注意力模块的图像描述生成模型

图像描述生成模型是使用自然语言描述图片的内容及其属性之间关系的算法模型.对现有模型描述质量不高、图片重要部分特征提取不足和模型过于复杂的问题进行了研究,提出了一种基于卷积块注意力机制模块(CBAM)的图像描述生成模型.该模型采用编码器-解码器结构,在特征提取网络Inception-v4中加入CBAM,并作为编码器提取图...

Image Caption Generation Model Based on Convolutional Block Attention Module

The image caption generation model uses natural language to describe the content of images and the relationship between attributes. In the existing models, there are problems of low description quality, insufficient feature extraction of important parts of images, and high complexity. Therefore, this study proposes an image caption generation model based on a Convolutional Block Attention Module (CBAM), which has an encoder-decoder structure. CBAM is added into the feature extraction network Inception-v4 and as an encoder, extracts the important feature information of the images. The information is then sent into the Long Short-Term Memory (LSTM) of the decoder to generate the caption of the corresponding pictures. The MSCOCO2014 data set is applied to training and testing, and multiple evaluation criteria are used to evaluate the accuracy of the model. The experimental results show that the improved model has a higher evaluation criterion score than other models, and Model2 can better extract image features and generate a more accurate description.