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arXiv · 2511.20201

GHR-VQA: Graph-guided Hierarchical Relational Reasoning for Video Question Answering

Dionysia Danai Brilli·Dimitrios Mallis·Vassilis Pitsikalis·Petros Maragos
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Abstract

We propose GHR-VQA, Graph-guided Hierarchical Relational Reasoning for Video Question Answering (Video QA), a novel human-centric framework that incorporates scene graphs to capture intricate human-object interactions within video sequences. Unlike traditional pixel-based methods, each frame is represented as a scene graph and human nodes across frames are linked to a global root, forming the video-level graph and enabling cross-frame reasoning centered on human actors. The video-level graphs are then processed by Graph Neural Networks (GNNs), transforming them into rich, context-aware embeddings for efficient processing. Finally, these embeddings are integrated with question features in a hierarchical network operating across different abstraction levels, enhancing both local and global understanding of video content. This explicit human-rooted structure enhances interpretability by decomposing actions into human-object interactions and enables a more profound understanding of spatiotemporal dynamics. We validate our approach on the Action Genome Question Answering (AGQA) dataset, achieving significant performance improvements, including a 7.3% improvement in object-relation reasoning over the state of the art.

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Dionysia Danai Brilli, Dimitrios Mallis, Vassilis Pitsikalis, Petros Maragos. 2025-11-25. GHR-VQA: Graph-guided Hierarchical Relational Reasoning for Video Question Answering. https://arxiv.org/abs/2511.20201

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