Current Text-to-Image (T2I) generation methods struggle to accurately create images with complex object relationships and scene compositions. To overcome these challenges, we propose KAIG, a novel text-to-image generative model that integrates a knowledge graph into the image generation process. Unlike traditional models, KAIG uses structured knowledge to enhance the retrieval of relevant information, enabling the generation of high-quality, contextually rich, and semantically consistent images from multi-modal inputs. We introduce a two-stage training strategy: first, condition adapters are trained to align multi-modal inputs, followed by fine-tuning the entire diffusion model. This approach ensures precise alignment between retrieved conditions and the image generation process, leading to an efficient and scalable pipeline. Our experiments on two popular datasets, MS-COCO and CUB-200-2011, show that KAIG consistently outperforms existing methods in both image quality and consistency. Notably, KAIG can seamlessly integrate with any pre-trained diffusion model, requiring minimal additional training while achieving superior results. Ultimately, KAIG demonstrates strong potential for addressing key limitations in current T2I models and advancing the field of image synthesis.

In the past decade, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) for undertaking ocean missions in dynamic, complex maritime environments. The success of these missions largely relies on motion planning algorithms that can generate optimal navigational trajectories to guide a USV. Apart from minimising the distance of a path, when deployed a USVs’ motion planning algorithms also need to consider other constraints such as energy consumption, the affected of ocean currents as well as the fast collision avoidance capability. In this paper, we propose a new algorithm named anisotropic GPMP2 to revolutionise motion planning for USVs based upon the fundamentals of GP (Gaussian process) motion planning (GPMP, or its updated version GPMP2). Firstly, we integrated the anisotropy into GPMP2 to make the generated trajectories follow ocean currents where necessary to reduce energy consumption on resisting ocean currents. Secondly, to further improve the computational speed and trajectory quality, a dynamic fast GP interpolation is integrated in the algorithm. Finally, the new algorithm has been validated on a WAM-V 20 USV in a ROS environment to show the practicability of anisotropic GPMP2. Note to Practitioners—The work reported in this article will be significant for USVs to conduct missions in complex, dynamic maritime environments where various obstacles and time-varying ocean currents exit. We develop this novel motion planning algorithm based on Gaussian process and optimise the trajectory using probabilistic inferences. The new algorithm can generate collision free trajectories that also minimise the influences caused by adverse ocean currents in a highly efficient way. In addition, the planning has been undertaken in a continuous-time domain making the generated trajectory have a guaranteed smoothness and readily feasible for autopilots to track. We use a coastal area with time-varying vortexes to present a challenging practical maritime environment. The presented algorithm integrates the available information about a fluid field regarding energy consumption and hazard level, along with the density of obstacles to plan a navigational route efficiently. To increase the practical performance of the proposed method, diverse models for generating ocean currents need to be developed in the future to tackle unpredictable situations.