A survey on dynamic scene understanding using temporal knowledge graphs: From scene knowledge representation to extrapolation

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2025-03-12 DOI:10.1016/j.neucom.2025.129854
Lu Linnan , Si Guannan , Liang Xinyu , Li Mingshen , Zhou Fengyu
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Abstract

Dynamic scene understanding is the process of extracting information from video, identifying and inferring entities and relations within the scene, with the aim of thoroughly analyzing complex scenes that evolve over time. This process leverages temporal knowledge graphs to achieve a deep and comprehensive understanding of dynamic environments and is widely applied in areas such as autonomous driving, surveillance, and video analysis. Initially, scene knowledge representation is explored as the foundational step in dynamic scene understanding, achieved through the generation of temporal knowledge graphs. These graphs are categorized based on temporal granularity. Temporal knowledge graphs are divided into multiple-frame dynamic graphs and single-frame dynamic graphs. The generation methods for multiple-frame dynamic graphs are categorized into fragment-based and sliding-window approaches, while single-frame dynamic graphs primarily utilize transformer-based methods. This section provides an overview of the generation models for temporal knowledge graphs. Subsequently, dynamic scenes are further analyzed using extrapolation methods, which are classified into entity-based and relation-based modeling approaches. Entity-based modeling methods mainly include temporal point processes and graph neural network techniques, while relation-based modeling focuses on reinforcement learning and meta-learning techniques. This section summarizes various existing extrapolation techniques within these categories. Finally, the paper discusses the challenges associated with temporal knowledge graphs and explores potential research directions, offering insights into future advancements in dynamic scene understanding.
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使用时态知识图谱的动态场景理解研究:从场景知识表示到推断
动态场景理解是从视频中提取信息、识别和推断场景中的实体和关系的过程,目的是彻底分析随时间演变的复杂场景。这一过程利用时态知识图谱实现对动态环境的深入和全面理解,被广泛应用于自动驾驶、监控和视频分析等领域。首先,作为动态场景理解的基础步骤,通过生成时态知识图谱来探索场景知识表示。这些图根据时间粒度进行分类。时态知识图分为多帧动态图和单帧动态图。多帧动态图的生成方法分为基于片段的方法和基于滑动窗口的方法,而单帧动态图则主要采用基于变换器的方法。本节概述了时态知识图谱的生成模型。随后,使用外推法进一步分析动态场景,外推法分为基于实体的建模方法和基于关系的建模方法。基于实体的建模方法主要包括时间点过程和图神经网络技术,而基于关系的建模方法则侧重于强化学习和元学习技术。本节总结了这些类别中现有的各种外推技术。最后,本文讨论了与时态知识图谱相关的挑战,并探讨了潜在的研究方向,为动态场景理解的未来发展提供了见解。
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
期刊最新文献
Brain state model: A novel method to represent the rhythmicity of object-specific selective attention from magnetoencephalography data A survey on dynamic scene understanding using temporal knowledge graphs: From scene knowledge representation to extrapolation Joint subspace learning and subspace clustering based unsupervised feature selection MAD-DGTD: Multivariate time series Anomaly Detection based on Dynamic Graph structure learning with Time Delay GDoT: A gated dual domain transformer for enhanced MRI off-resonance correction
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