13 citations · 14 across the 3 of their papers we have counts for
8 papers · 1 filter
Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation
Tianyu Li, Subhankar Roy, Huayi Zhou +2
To overcome the domain gap between synthetic and real-world datasets, unsupervised domain adaptation methods have been proposed for semantic segmentation. Majority of the previous…
Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation
Yangsong Zhang, Subhankar Roy, Hongtao Lu +2
In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unann…
Neighborhood Contrastive Learning for Novel Class Discovery
Zhun Zhong, Enrico Fini, Subhankar Roy +3
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the pe…
Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation
Subhankar Roy, Evgeny Krivosheev, Zhun Zhong +2
In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the…
TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation
Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto +2
Most domain adaptation methods consider the problem of transferring knowledge to the target domain from a single source dataset. However, in practical applications, we typically ha…
Motion-supervised Co-Part Segmentation
Aliaksandr Siarohin, Subhankar Roy, Stéphane Lathuilière +3
Recent co-part segmentation methods mostly operate in a supervised learning setting, which requires a large amount of annotated data for training. To overcome this limitation, we p…