Abstract
Typical techniques for sequence classification are designed for well-segmented sequences which have been edited to remove noisy or irrelevant parts. Therefore, such methods cannot be easily applied on noisy sequences expected in real-world applications. In this paper, we present the Temporal Attention-Gated Model (TAGM) which integrates ideas from attention models and gated recurrent networks to better deal with noisy or unsegmented sequences. Specifically, we extend the concept of attention model to measure the relevance of each observation (time step) of a sequence. We then use a novel gated recurrent network to learn the hidden representation for the final prediction. An important advantage of our approach is interpretability since the temporal attention weights provide a meaningful value for the salience of each time step in the sequence. We demonstrate the merits of our TAGM approach, both for prediction accuracy and interpretability, on three different tasks: spoken digit recognition, text-based sentiment analysis and visual event recognition.
Original language | English |
---|---|
Title of host publication | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
Editors | L. O'Conner |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 820-829 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-5386-0457-1 |
ISBN (Print) | 978-1-5386-0458-8 |
DOIs | |
Publication status | Published - 2017 |
Event | 30th IEEE Conference on Computer Vision and Pattern Recognition - Honolulu, United States Duration: 21 Jul 2017 → 26 Jul 2017 |
Conference
Conference | 30th IEEE Conference on Computer Vision and Pattern Recognition |
---|---|
Abbreviated title | CVRP 2017 |
Country/Territory | United States |
City | Honolulu |
Period | 21/07/17 → 26/07/17 |
Keywords
- Hidden Markov models
- Logic gates
- Noise measurement
- Mathematical model
- Computational modeling
- Data models