no code implementations • ICCV 2023 • Samitha Herath, Basura Fernando, Ehsan Abbasnejad, Munawar Hayat, Shahram Khadivi, Mehrtash Harandi, Hamid Rezatofighi, Gholamreza Haffari
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-invariant representations.
no code implementations • CVPR 2021 • Basura Fernando, Samitha Herath
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.
1 code implementation • CVPR 2021 • Islam Nassar, Samitha Herath, Ehsan Abbasnejad, Wray Buntine, Gholamreza Haffari
We train two classifiers with two different views of the class labels: one classifier uses the one-hot view of the labels and disregards any potential similarity among the classes, while the other uses a distributed view of the labels and groups potentially similar classes together.
no code implementations • CVPR 2019 • Samitha Herath, Mehrtash Harandi, Basura Fernando, Richard Nock
In practice, this is achieved by minimizing the disparity between the domains, usually measured in terms of their statistical properties.
no code implementations • CVPR 2017 • Samitha Herath, Mehrtash Harandi, Fatih Porikli
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.
no code implementations • 16 May 2016 • Samitha Herath, Mehrtash Harandi, Fatih Porikli
Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation.