  This project intends to study the image representation based on attention
mechanism and multimodal data. By adding multiple pattern layers to the
attribute model, the semantic and hidden layers of image content are
integrated. The word vector is quantified by the Word2Vec method and then
evaluated by a word embedding convolutional neural network. The published
experimental results of the two groups were tested. The experimental results
show that this method can convert discrete features into continuous characters,
thus reducing the complexity of feature preprocessing. Word2Vec and natural
language processing technology are integrated to achieve the goal of direct
evaluation of missing image features. The robustness of the image feature
evaluation model is improved by using the excellent feature analysis
characteristics of a convolutional neural network. This project intends to
improve the existing image feature identification methods and eliminate the
subjective influence in the evaluation process. The findings from the
simulation indicate that the novel approach has developed is viable,
effectively augmenting the features within the produced representations.
