activity
20202026
most citedSkeleton-based Action Recognition via Spatial and Temporal Transformer Networks

406 citations · 668 across the 15 of their papers we have counts for

collaborators

17 papers

cs.CV2026

Long Time No See: Benchmarking VLMs for Out-of-Sight Spatiotemporal Reasoning in Egocentric Videos

Fangzhou Ma, Ivo Alexander Ban, Eren Homburg +5

Real-world AI systems must reason about objects that are no longer visible: an AR assistant guiding a user back to an object used earlier, a household robot retrieving an item some…

cs.CV2026

DAPEVO: Deep Adaptive Patch Frame-Event Visual Odometry

Luca Gandolfi, Simone Nascivera, Roberto Pellerito +3

Visual odometry is essential for autonomous navigation in GPS-denied environments, yet RGB-based methods remain vulnerable to motion blur, challenging illumination, and dropped fra…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025★ 1 cited

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…

cs.CV2024

Spatial Cognition from Egocentric Video: Out of Sight, Not Out of Mind

Chiara Plizzari, Shubham Goel, Toby Perrett +3

As humans move around, performing their daily tasks, they are able to recall where they have positioned objects in their environment, even if these objects are currently out of the…

cs.CV2023★ 2 cited

An Outlook into the Future of Egocentric Vision

Chiara Plizzari, Gabriele Goletto, Antonino Furnari +5

What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with…

cs.CV2023

EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge: Mixed Sequences Prediction

Amirshayan Nasirimajd, Simone Alberto Peirone, Chiara Plizzari +1

This report presents the technical details of our approach for the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. Our approach is based on…

cs.CV2023

What can a cook in Italy teach a mechanic in India? Action Recognition Generalisation Over Scenarios and Locations

Chiara Plizzari, Toby Perrett, Barbara Caputo +1

We propose and address a new generalisation problem: can a model trained for action recognition successfully classify actions when they are performed within a previously unseen sce…

cs.CV2021★ 4 cited

E(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition

Chiara Plizzari, Mirco Planamente, Gabriele Goletto +4

Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Due to their sensing mechanism, event cameras have…

cs.CV2021

Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition

Mirco Planamente, Chiara Plizzari, Emanuele Alberti +1

First person action recognition is becoming an increasingly researched area thanks to the rising popularity of wearable cameras. This is bringing to light cross-domain issues that…

cs.CV2021★ 2 cited

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…

cs.CV2021★ 6 cited

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…

cs.CV2021

DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation

Mirco Planamente, Chiara Plizzari, Marco Cannici +5

Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". The innovative way they acquire data presents seve…

cs.CV2020★ 247 cited

Spatial Temporal Transformer Network for Skeleton-based Action Recognition

Chiara Plizzari, Marco Cannici, Matteo Matteucci

Skeleton-based human action recognition has achieved a great interest in recent years, as skeleton data has been demonstrated to be robust to illumination changes, body scales, dyn…

cs.CV2020★ 406 cited

Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks

Chiara Plizzari, Marco Cannici, Matteo Matteucci

Skeleton-based Human Activity Recognition has achieved great interest in recent years as skeleton data has demonstrated being robust to illumination changes, body scales, dynamic c…