Abstract
Ultra-long egocentric videos spanning multiple days present significant challenges for video understanding. Existing approaches still rely on fragmented local processing and limited temporal modeling, restricting their ability to reason over such extended sequences. To address these limitations, we introduce EgoGraph, a training-free and dynamic knowledge-graph construction framework that explicitly encodes long-term, cross-entity dependencies in egocentric video streams. EgoGraph employs a novel egocentric schema that unifies the extraction and abstraction of core entities, such as people, objects, locations, and events, and structurally reasons about their attributes and interactions, yielding a significantly richer and more coherent semantic representation than traditional clip-based video models. Crucially, we develop a temporal relational modeling strategy that captures temporal dependencies across entities and accumulates stable long-term memory over multiple days, enabling complex temporal reasoning. Extensive experiments on the EgoLifeQA and EgoR1-bench benchmarks demonstrate that EgoGraph achieves state-of-the-art performance on long-term video question answering, validating its effectiveness as a new paradigm for ultra-long egocentric video understanding.
Abstract (translated)
超长的第一人称视频（跨越多天）对视频理解提出了重大挑战。现有的方法仍然依赖于片段化的局部处理和有限的时间建模，限制了它们在如此长时间序列上进行推理的能力。为了解决这些局限性，我们引入了一个名为EgoGraph的框架，这是一个无需训练且动态构建知识图谱的方法，该方法明确地编码了长期跨实体之间的依赖关系。EgoGraph采用了一种新颖的第一人称模式，统一提取和抽象核心实体（如人物、物体、地点和事件），并结构化推理这些实体的属性及互动，从而比传统的基于片段的视频模型提供更丰富且连贯的语义表示。至关重要的是，我们开发了一种时间关系建模策略，捕捉了跨多个实体的时间依赖性，并累积了跨越多天的稳定长期记忆，这使得复杂的时序推理成为可能。 在EgoLifeQA和EgoR1-bench基准测试上的大量实验表明，EgoGraph在长视频问答方面达到了最先进的性能水平，验证了它作为理解超长第一人称视频的新范式的有效性。
URL
https://arxiv.org/abs/2602.23709
