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Found 6 papers, 1 papers with code
Energy-based Self-Training and Normalization for Unsupervised Domain Adaptation
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.
Anticipating human actions by correlating past with the future with Jaccard similarity measures
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.
All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
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.
Min-Max Statistical Alignment for Transfer Learning
In practice, this is achieved by minimizing the disparity between the domains, usually measured in terms of their statistical properties.
Learning an Invariant Hilbert Space for Domain Adaptation
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.
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation.
