Cross-domain activity recognition

By Vincent Wenchen Zheng, Derek Hao Hu, Qiang Yang. 2009

140 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach (2010, methodology): “This matrix can be obtained by using the common sense on the Web, with some search-engine based similarity methods proposed in (Zheng, Hu, and Yang 2009) and (Wyatt, Philipose, and Choudhury 2005).”
Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning (2016, background): “Some studies (Cook et al. 2013; Hu and Yang 2011; Zheng et al. 2009) tried to alleviate this problem by transferring labelled data from one activity recognition task to another, but such studies were limited to transferring between physical sensor data only.”
Transfer Learning for Activity Recognition: A Survey (2013, methodology): “Research into transfer-based activity recognition using ambient sensors has almost exclusively focused on uninformed supervised transfer learning [14,25,26,54,66,67,69,80], but a few algorithms are able to take advantage of the labeled target data if it is available [55–57].”
Collaborative location and activity recommendations with GPS history data (2010, background): “Most of the previous work related to activity study focuses on how to recognize an activity from sensor data by ubiquitous computing [21].”
Web-Based Classifiers for Human Action Recognition (2012, methodology): “Recent works on cross-domain learning (such as [43] and [44]) are closely related to our work, since they try to transfer a model that is learned from one domain and for use in another domain.”
A Survey of Zero-Shot Learning (2019, methodology): “For example, in CDAR [178], for an unseen class, instances from all seen classes are borrowed with the corresponding similarities computed based on prototypes as the weights.”
A Survey of Transfer and Multitask Learning in Bioinformatics (2011, background): “, image [9] and video [10] analysis), and ubiquitous computing [11, 12].”
Feature-based transfer learning with real-world applications (2010, background): “[228] proposed to apply transfer learning techniques for solving indoor sensor-based activity recognition problems, respectively.”