most citedUnsupervised Domain Adaptation for Video Transformers in Action Recognition

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

collaborators

5 papers

cs.CV20232 cited

The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation

Giacomo Zara, Alessandro Conti, Subhankar Roy +3

Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target datas…

cs.CV2023

Rotation Synchronization via Deep Matrix Factorization

Gk Tejus, Giacomo Zara, Paolo Rota +3

In this paper we address the rotation synchronization problem, where the objective is to recover absolute rotations starting from pairwise ones, where the unknowns and the measures…

cs.CV2023

AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation

Giacomo Zara, Subhankar Roy, Paolo Rota +1

Open-set Unsupervised Video Domain Adaptation (OUVDA) deals with the task of adapting an action recognition model from a labelled source domain to an unlabelled target domain that…

cs.CV2023

Simplifying Open-Set Video Domain Adaptation with Contrastive Learning

Giacomo Zara, Victor Guilherme Turrisi da Costa, Subhankar Roy +2

In an effort to reduce annotation costs in action recognition, unsupervised video domain adaptation methods have been proposed that aim to adapt a predictive model from a labelled…

cs.CV20224 cited

Unsupervised Domain Adaptation for Video Transformers in Action Recognition

Victor G. Turrisi da Costa, Giacomo Zara, Paolo Rota +4

Over the last few years, Unsupervised Domain Adaptation (UDA) techniques have acquired remarkable importance and popularity in computer vision. However, when compared to the extens…