The general silhouette‐based gait recognition methods usually rely on binary human silhouette, which is easily affected by external factors, making it unsuitable for situations while wearing heavy clothes or carrying objects, etc. In this study, a new skeleton‐based gait recognition model is proposed. The model first extracts the spatial and temporal features of gait using the space and time relationship between body joints, and second, it eliminates redundant features by decomposing the feature map, to achieve a better recognition accuracy in the presence of external factors. Through abundant experiments on two common datasets, CASIA‐B and OUMVLP‐Pose, the proposed model has been proved to have higher recognition accuracy and remarkable robustness.

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.

Automatic image captioning has achieved a great progress. However, the existing captioning frameworks basically enumerate the objects in the image. The generated captions lack the real-world knowledge about named entities and their relations, such as the relations among famous persons, organizations and buildings. On the contrary, humans interpret images in a specific way by providing real-world knowledge with relations of the aforementioned named entities. To generate human-like captions, we focus on captioning the images of news, which could provide real-world knowledge of the whole story behind the images. Then we propose a novel model that makes captions closer to the human-like description of the image, by leveraging the semantic relevance of the named entities. The named entities are not only extracted from news under the guidance of the image content, but also extended with external knowledge based on the semantic relations. In detail, we propose a sentence correlation analysis algorithm to selectively draw the contextual information from news, and use entity-linking algorithm based on knowledge graph to discover the relations of entities with a global sight. The results of extensive experiments based on real-world dataset which is collected from the news show that our model generates image captions closer to the corresponding real-world captions.

Image-text matching using the image caption method has made a great progress. However, there are many named entities in news text, and existing approaches are unable to directly generate named entities in the news image caption. It leads to a semantic gap between text and news image caption. Moreover, the existing methods lack the analysis of indirect relations between named entities. Therefore those approaches easily leads to relations error when generating news image caption. To generate the news image caption with named entities by analyzing the indirect relations between named entities. We propose a novel model. In details, we propose the TopNews dataset with related news, which aims to construct the relations between named entities as widely as possible. Then we develop the news knowledge graph by extracting named entities from TopNews dataset. Furthermore, we propose News Knowledge Driven Graph Neural Network (NKD-GNN). We utilize NKD-GNN to analyzing the whole relations of entities in news knowledge graph. In this way, we generate the news image caption with named entities. The results of extensive experiments based on TopNews dataset and common dataset demonstrate that our approach is effective in detecting the consistency of news images and text.