Understanding human actions in visual data is tied to advances in complementary research
areas including object recognition, human dynamics, domain adaptation and semantic …

Abstract Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a different but …

When the training and the test data belong to different domains, the accuracy of an object
classifier is significantly reduced. Therefore, several algorithms have been proposed in the …

In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

Currently, unsupervised heterogeneous domain adaptation in a generalized setting, which
is the most common scenario in real-world applications, is under insufficient exploration …

Real time crowd analysis represents an active area of research within the computer vision
community in general and scene analysis in particular. Over the last 10 years, various …

In real-world transfer learning tasks, especially in cross-modal applications, the source
domain and the target domain often have different features and distributions, which are well …

To bridge source and target domains for domain adaptation, there are three important types
of information including data structure, domain label, and class label. Most existing domain …

We propose a novel reconstruction-based transfer learning method called latent sparse
domain transfer (LSDT) for domain adaptation and visual categorization of heterogeneous …

While domain adaptation (DA) aims to associate the learning tasks across data domains,
heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain …