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.

Safe and efficient navigation of a robot in a high-density and dynamic crowd is a challenging task. Most of existing navigation algorithms need to acquire the full dynamics of neighboring humans at all times, making them heavily dependent on a complex upper level information state estimation process. Moreover, the scene perception modules of existing algorithms do not comprehensively model human–robot interaction in the spatiotemporal dimension, leading to frequent freezing and collision problems in reinforcement learning-based navigation algorithms. To address the above problems, we propose a fine-grained spatiotemporal graphical attention navigation algorithm (FST-RL), which enriches the scene perception module of reinforcement learning algorithms by jointly encoding human–robot motion patterns, interhuman relations, and long-term dependent information of human–robot interactions. With the proposed algorithm, socially compatible navigation routes can be generated by the built-in spatiotemporal reasoning module with the premise of obtaining only the position information of agents in the robot’s perception domain. The experimental results show a significant improvement of FST-RL in terms of success rate (22.3% improvement), navigation time (13.6% reduction), and average return (20.8% improvement) in a high-density human environment compared with the current optimal navigation algorithm (DS-RNN). Ablation and qualitative experiments show that the scene perception module of FST-RL can effectively reduce the robot’s collision and conservative behaviors in challenging scenarios.