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20182025
most citedCross-Domain First Person Audio-Visual Action Recognition through Relative Norm Alignment

6 citations · 8 across the 5 of their papers we have counts for

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9 papers · 1 filter

cs.CV2025

FS-SAM2: Adapting Segment Anything Model 2 for Few-Shot Semantic Segmentation via Low-Rank Adaptation

Bernardo Forni, Gabriele Lombardi, Federico Pozzi +1

Few-shot semantic segmentation has recently attracted great attention. The goal is to develop a model capable of segmenting unseen classes using only a few annotated samples. Most…

cs.CV2024

Egocentric zone-aware action recognition across environments

Simone Alberto Peirone, Gabriele Goletto, Mirco Planamente +3

Human activities exhibit a strong correlation between actions and the places where these are performed, such as washing something at a sink. More specifically, in daily living envi…

cs.CV2022

PoliTO-IIT-CINI Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition

Mirco Planamente, Gabriele Goletto, Gabriele Trivigno +2

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.CV20212 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.CV20216 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…