Samitha Herath — accepted AI-conference papers

2023

Energy-based Self-Training and Normalization for Unsupervised Domain Adaptation

Samitha Herath, Basura Fernando, Ehsan Abbasnejad, Munawar Hayat, Shahram Khadivi, Mehrtash Harandi +2

ICCV 2023poster

We propose an Unsupervised Domain Adaptation (UDA) method by making use of Energy-Based Learning (EBL) and demonstrate 1. EBL can be used to improve the instance selection for a self-training task on the unlabelled target domain, and 2. alignment and normalizing energy scores can learn domain-invari…

Cited by 9 PDF ↗Scholar ↗

2021

All Labels Are Not Created Equal: Enhancing Semi-Supervision via Label Grouping and Co-Training

Islam Nassar, Samitha Herath, Ehsan Abbasnejad, Wray Buntine, Gholamreza Haffari

CVPR 2021poster

Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on iteratively using the model to generate artificial labels for the unlabeled data to train against. A common property among its various methods is that they only rely on the model's prediction to make labeling decision…

Cited by 71 PDF ↗code ↗Scholar ↗

2021

Anticipating Human Actions by Correlating Past With the Future With Jaccard Similarity Measures

Basura Fernando, Samitha Herath

CVPR 2021poster

We propose a framework for early action recognition and anticipation by correlating past features with the future using three novel similarity measures called Jaccard vector similarity, Jaccard cross-correlation and Jaccard Frobenius inner product over covariances. Using these combinations of novel…

Cited by 76 PDF ↗Scholar ↗

2019

Min-Max Statistical Alignment for Transfer Learning

Samitha Herath, Mehrtash Harandi, Basura Fernando, Richard Nock

CVPR 2019poster

A profound idea in learning invariant features for transfer learning is to align statistical properties of the domains. In practice, this is achieved by minimizing the disparity between the domains, usually measured in terms of their statistical properties. We question the capability of this school…

Cited by 13 PDF ↗Scholar ↗

2017

Learning an Invariant Hilbert Space for Domain Adaptation

Samitha Herath, Mehrtash Harandi, Fatih Porikli

CVPR 2017poster

This paper introduces a learning scheme to construct a Hilbert space (i.e., a vector space along its inner product) to address both unsupervised and semi-supervised domain adaptation problems. This is achieved by learning projections from each domain to a latent space along the Mahalanobis metric of…

Cited by 149 PDF ↗Scholar ↗