activity
20192021
most citedNeighborhood Contrastive Learning for Novel Class Discovery

13 citations · 13 across the 1 of their papers we have counts for

collaborators

7 papers

cs.CV202113 cited

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…

cs.CV2021

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…

cs.CV2020

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…

cs.CV2020

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…

cs.NE2019

Regularized Evolutionary Algorithm for Dynamic Neural Topology Search

Cristiano Saltori, Subhankar Roy, Nicu Sebe +1

Designing neural networks for object recognition requires considerable architecture engineering. As a remedy, neuro-evolutionary network architecture search, which automatically se…

cs.CV2019

Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images

Subhankar Roy, Enver Sangineto, Begüm Demir +1

Hashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed. The traditional hash…