Search Results for author:
Found 12 papers, 4 papers with code
Spatial Cognition from Egocentric Video: Out of Sight, Not Out of Mind
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 sight.
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge: Mixed Sequences Prediction
As only unlabelled target data are available under the UDA setting, we use a standard pseudo-labeling strategy for extracting action labels for the target.
What can a cook in Italy teach a mechanic in India? Action Recognition Generalisation Over Scenarios and Locations
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 scenario and in a previously unseen location?
E2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition
However, the ever-growing field of event-based vision has, to date, overlooked the potential of event cameras in such applications.
E$^2$(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition
However, the ever-growing field of event-based vision has, to date, overlooked the potential of event cameras in such applications.
Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition
First person action recognition is becoming an increasingly researched area thanks to the rising popularity of wearable cameras.
PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition
In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition.
Cross-Domain First Person Audio-Visual Action Recognition through Relative Norm Alignment
First person action recognition is an increasingly researched topic because of the growing popularity of wearable cameras.
DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation
Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events".
Spatial Temporal Transformer Network for Skeleton-based Action Recognition
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, dynamic camera views, and complex background.
Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks
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 camera views, and complex background.
