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arXiv · 2506.08553

From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge

Agnese Taluzzi·Davide Gesualdi·Riccardo Santambrogio·Chiara Plizzari·Francesca Palermo·Simone Mentasti·Matteo Matteucci
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Abstract

This report presents SceneNet and KnowledgeNet, our approaches developed for the HD-EPIC VQA Challenge 2025. SceneNet leverages scene graphs generated with a multi-modal large language model (MLLM) to capture fine-grained object interactions, spatial relationships, and temporally grounded events. In parallel, KnowledgeNet incorporates ConceptNet's external commonsense knowledge to introduce high-level semantic connections between entities, enabling reasoning beyond directly observable visual evidence. Each method demonstrates distinct strengths across the seven categories of the HD-EPIC benchmark, and their combination within our framework results in an overall accuracy of 44.21% on the challenge, highlighting its effectiveness for complex egocentric VQA tasks.

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Agnese Taluzzi, Davide Gesualdi, Riccardo Santambrogio, Chiara Plizzari, Francesca Palermo, Simone Mentasti, Matteo Matteucci. 2025-06-10. From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge. https://arxiv.org/abs/2506.08553

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