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

• URL: http://arxiv.org/abs/2511.20201v1
• Date: Tue, 25 Nov 2025 11:24:25 GMT
• Title: GHR-VQA: Graph-guided Hierarchical Relational Reasoning for Video Question Answering
• Authors: Dionysia Danai Brilli, Dimitrios Mallis, Vassilis Pitsikalis, Petros Maragos,
• Abstract summary: We propose a novel framework that incorporates graphs capture human-object interactions within video sequences.<n>Unlike traditional methods, each frame is represented as intricate and graph human nodes across frames are linked to a scene.<n>This human-rooted structure enhances interpretability by decomposing into human-object interactions.
• Score: 15.887744981283179
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Language-guided Recursive Spatiotemporal Graph Modeling for Video Summarization [47.65036144170475]
  Video summarization aims to selects that are visually diverse and represent the whole story of a given video.<n>We present VideoGraph, which formulates the objects and frames as nodes of the spatial and temporal graphs.<n>In our experiments, VideoGraph achieves state-of-the-art performance on several benchmarks for generic and querylink video summarization.
  arXiv  Detail & Related papers  (2025-09-06T05:37:31Z)
• Understanding Long Videos via LLM-Powered Entity Relation Graphs [51.13422967711056]
  GraphVideoAgent is a framework that maps and monitors the evolving relationships between visual entities throughout the video sequence.<n>Our approach demonstrates remarkable effectiveness when tested against industry benchmarks.
  arXiv  Detail & Related papers  (2025-01-27T10:57:24Z)
• Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering [13.294004180200496]
  We introduce Local-Global Question Aware Video Embedding (LGQAVE), which incorporates three major innovations to integrate multi-modal knowledge better.<n>LGQAVE moves beyond traditional ad-hoc frame sampling by utilizing a cross-attention mechanism that precisely identifies the most relevant frames concerning the questions.<n>An additional cross-attention module integrates these local and global embeddings to generate the final video embeddings, which a language model uses to generate answers.
  arXiv  Detail & Related papers  (2024-12-12T12:39:07Z)
• HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding [8.10024991952397]
  Existing methods focus on complex interactivities while leveraging a simple relationship model. We propose a new approach named Hierarchical Interlacement Graph (HIG), which leverages a unified layer and graph within a hierarchical structure. Our approach demonstrates superior performance to other methods through extensive experiments conducted in various scenarios.
  arXiv  Detail & Related papers  (2023-12-05T18:47:19Z)
• Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition [55.7731053128204]
  In given video-based Human-Object Interaction scene, modeling thetemporal relationship between humans and objects are the important cue to understand the contextual information presented in the video. With the effective-temporal relationship modeling, it is possible not only to uncover contextual information in each frame but also directly capture inter-time dependencies. The full use of appearance features, spatial location and the semantic information are also the key to improve the video-based Human-Object Interaction recognition performance.
  arXiv  Detail & Related papers  (2021-08-19T11:57:27Z)
• Location-aware Graph Convolutional Networks for Video Question Answering [85.44666165818484]
  We propose to represent the contents in the video as a location-aware graph. Based on the constructed graph, we propose to use graph convolution to infer both the category and temporal locations of an action. Our method significantly outperforms state-of-the-art methods on TGIF-QA, Youtube2Text-QA, and MSVD-QA datasets.
  arXiv  Detail & Related papers  (2020-08-07T02:12:56Z)
• SumGraph: Video Summarization via Recursive Graph Modeling [59.01856443537622]
  We propose graph modeling networks for video summarization, termed SumGraph, to represent a relation graph. We achieve state-of-the-art performance on several benchmarks for video summarization in both supervised and unsupervised manners.
  arXiv  Detail & Related papers  (2020-07-17T08:11:30Z)
• Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning [72.52804406378023]
  Cross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning model, which decomposes video-text matching into global-to-local levels.
  arXiv  Detail & Related papers  (2020-03-01T03:44:19Z)
• Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks [150.5425122989146]
  This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS) AGNN builds a fully connected graph to efficiently represent frames as nodes, and relations between arbitrary frame pairs as edges. Experimental results on three video segmentation datasets show that AGNN sets a new state-of-the-art in each case.
  arXiv  Detail & Related papers  (2020-01-19T10:45:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.