Predicting the future trajectories of multiple agents is essential for various applications in real life, such as surveillance systems, autonomous driving, and social robots. The trajectory prediction task is influenced by many factors, including the individual historical trajectory, interactions between agents, and the fuzzy nature of the observed agents’ motion. While existing methods have made great progress on the topic of trajectory prediction, they treat all the information uniformly, which limits the effectiveness of information utilization. To this end, in this paper, we propose and utilize a model-agnostic framework to regard all the information in a two-level hierarchical view. Particularly, the first-level view is the inter-trajectory view. In this level, we observe that the difficulty in predicting different trajectory samples varies. We define trajectory difficulty and train the proposed framework in an “easy-to-hard” schema. The second-level view is the intra-trajectory level. We find the influencing factors for a particular trajectory can be divided into two parts. The first part is global features, which keep stable within a trajectory, i.e., the expected destination. The second part is local features, which change over time, i.e., the current position. We believe that the two types of information should be handled in different ways. The hierarchical view is beneficial to take full advantage of the information in a fine-grained way. Experimental results validate the effectiveness of the proposed model-agnostic framework.

Knowledge graphs are essential tools for representing real-world facts and finding wide applications in various domains. However, the process of constructing knowledge graphs often introduces noises and errors, which can negatively impact the performance of downstream applications. Current methods for knowledge graph error detection primarily focus on graph structure and overlook the importance of textual information in error detection. Therefore, this paper proposes a novel error detection framework that combines both structural and textual information. The framework utilizes a confidence module for error detection while generating knowledge embeddings. The performance of this approach outperforms baseline methods in error detection and link prediction experiments, particularly achieving state-of-the-art performance in the error detection task.