6 citations · 8 across the 4 of their papers we have counts for
8 papers · 1 filter
Graph it first! Enabling Reasoning on Long-form Egocentric Videos through Scene Graphs
Agnese Taluzzi, Riccardo Santambrogio, Simone Mentasti +2
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…
Domain Generalization using Action Sequences for Egocentric Action Recognition
Amirshayan Nasirimajd, Chiara Plizzari, Simone Alberto Peirone +3
Recognizing human activities from visual inputs, particularly through a first-person viewpoint, is essential for enabling robots to replicate human behavior. Egocentric vision, cha…
From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge
Agnese Taluzzi, Davide Gesualdi, Riccardo Santambrogio +4
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 lang…
Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos
Chiara Plizzari, Alessio Tonioni, Yongqin Xian +2
Understanding fine-grained temporal dynamics is crucial in egocentric videos, where continuous streams capture frequent, close-up interactions with objects. In this work, we bring…
PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition
Chiara Plizzari, Mirco Planamente, Emanuele Alberti +1
In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. To tackle the dom…
Cross-Domain First Person Audio-Visual Action Recognition through Relative Norm Alignment
Mirco Planamente, Chiara Plizzari, Emanuele Alberti +1
First person action recognition is an increasingly researched topic because of the growing popularity of wearable cameras. This is bringing to light cross-domain issues that are ye…