most citedUncertainty-guided Source-free Domain Adaptation

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

collaborators

5 papers

cs.CV20231 cited

RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation

Subhankar Roy, Riccardo Volpi, Gabriela Csurka +1

Class-incremental semantic image segmentation assumes multiple model updates, each enriching the model to segment new categories. This is typically carried out by providing expensi…

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.CV20223 cited

Uncertainty-guided Source-free Domain Adaptation

Subhankar Roy, Martin Trapp, Andrea Pilzer +4

Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data a…

cs.CV2022

Class-incremental Novel Class Discovery

Subhankar Roy, Mingxuan Liu, Zhun Zhong +2

We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a…