Graph it first! Enabling Reasoning on Long-form Egocentric Videos through Scene Graphs
Existing multi-modal large language models (MLLMs) face significant challenges in processing long video sequences due to strict input token limitations. As a result, current video understanding approaches, especially in egocentric settings characterized by complex dynamics, frequent state changes, and moving cameras, are forced to massively subsample frames. This leads to severe loss of temporal and contextual information, constraining their ability to perform fine-grained video reasoning. In this work, we introduce a framework for egocentric video question answering (VQA) that overcomes these input constraints through Egocentric Scene Graphs (EgoSGs), i.e., temporally grounded, structure...
| Authors | Agnese Taluzzi, Riccardo Santambrogio, Simone Mentasti et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 June 2026 |
| Last updated at source | 2 July 2026 |
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