Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2506.08553 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909645143015424 |
|---|---|
| author | Taluzzi, Agnese Gesualdi, Davide Santambrogio, Riccardo Plizzari, Chiara Palermo, Francesca Mentasti, Simone Matteucci, Matteo |
| author_facet | Taluzzi, Agnese Gesualdi, Davide Santambrogio, Riccardo Plizzari, Chiara Palermo, Francesca Mentasti, Simone Matteucci, Matteo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08553 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge Taluzzi, Agnese Gesualdi, Davide Santambrogio, Riccardo Plizzari, Chiara Palermo, Francesca Mentasti, Simone Matteucci, Matteo Computer Vision and Pattern Recognition 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. |
| title | From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.08553 |