Abstract / Description of output
Compared to machines, humans are extremely good at classifying images into categories, especially when they possess prior knowledge of the categories at hand. If this prior information is not available, supervision in the form of teaching images is required. To learn categories more quickly, people should see important and representative images first, followed by less important images later – or not at all. However, image-importance is individual-specific, i.e. a teaching image is important to a student if it changes their overall ability to discriminate between classes. Further, students keep learning, so while image-importance depends on their current knowledge, it also varies with time. In this work we propose an Interactive Machine Teaching algorithm that enables a computer to teach challenging visual concepts to a human. Our adaptive algorithm chooses, online, which labeled images from a teaching set should be shown to the student as they learn. We show that a teaching strategy that probabilistically models the student’s ability and progress, based on their correct and incorrect answers, produces better ‘experts’. We present results using real human participants across several varied and challenging real-world datasets.
Original language | English |
---|---|
Title of host publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 2616-2624 |
Number of pages | 9 |
ISBN (Electronic) | 978-1-4673-6964-0 |
DOIs | |
Publication status | Published - 15 Oct 2015 |
Event | 2015 IEEE Conference on Computer Vision and Pattern Recognition - Boston, United States Duration: 8 Jun 2015 → 10 Jun 2015 http://www.pamitc.org/cvpr15/ |
Publication series
Name | |
---|---|
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISSN (Print) | 1063-6919 |
ISSN (Electronic) | 1063-6919 |
Conference
Conference | 2015 IEEE Conference on Computer Vision and Pattern Recognition |
---|---|
Abbreviated title | CVPR 2015 |
Country/Territory | United States |
City | Boston |
Period | 8/06/15 → 10/06/15 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- computer aided instruction
- image classification
- image representation
- interactive systems
- teaching
- interactive multiclass machine teaching algorithm
- image categories
- prior knowledge
- teaching images
- representative images
- image-importance
- visual concepts
- adaptive algorithm
- labeled images
- teaching set
- teaching strategy
- student ability
- student progress
- Education
- Visualization
- Computational modeling
- Computers
- Testing
- Adaptation models
- Probabilistic logic