Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Exploring semi-supervised and active learning for activity recognition
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Cross-domain activity recognition
Proceedings of the 11th international conference on Ubiquitous computing
Human Activity Recognition and Pattern Discovery
IEEE Pervasive Computing
Tracking free-weight exercises
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing human actions by attributes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Interactively building a discriminative vocabulary of nameable attributes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Human action recognition by learning bases of action attributes and parts
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
An integrated framework for human activity classification
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Attribute learning for understanding unstructured social activity
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
NuActiv: recognizing unseen new activities using semantic attribute-based learning
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
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Understanding human activities is important for user-centric and context-aware applications. Previous studies showed promising results using various machine learning algorithms. However, most existing methods can only recognize the activities that were previously seen in the training data. In this paper, we present a new zero-shot learning framework for human activity recognition that can recognize an unseen new activity even when there are no training samples of that activity in the dataset. We propose a semantic attribute sequence model that takes into account both the hierarchical and sequential nature of activity data. Evaluation on datasets in two activity domains show that the proposed zero-shot learning approach achieves 70-75% precision and recall recognizing unseen new activities, and outperforms supervised learning with limited labeled data for the new classes.