Projects per year
Abstract
In this paper, we investigate whether it is possible to leverage information from multiple datasets when performing frame-based action recognition, which is an essential component of real-time activity monitoring systems. In particular, we investigate whether the training of an LSTM can benefit from pre-training or co-training on multiple datasets of related tasks when it uses non-transferred visual CNN features. A number of label mappings and multi-dataset training techniques are proposed and tested on three challenging kitchen activity datasets - Breakfast, 50 Salads and MPII Cooking 2. We show that transferring, by pre-training on similar datasets using label concatenation, delivers improved frame-based classification accuracy and faster training convergence than random initialisation.
Original language | English |
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Title of host publication | 2017 IEEE International Conference of Computer Vision Workshop (ICCVW 2017) |
Subtitle of host publication | Proceedings of a meeting held 22-29 October 2017, Venice, Italy |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1354-1362 |
Number of pages | 9 |
ISBN (Electronic) | 9781538610343 |
ISBN (Print) | 9781538610350 |
DOIs | |
Publication status | Published - Feb 2018 |
Event | International Conference on Computer Vision Workshops (ICCVW), - Venice, Italy Duration: 22 Oct 2017 → … |
Publication series
Name | |
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ISSN (Print) | 2473-9444 |
Conference
Conference | International Conference on Computer Vision Workshops (ICCVW), |
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Country/Territory | Italy |
City | Venice |
Period | 22/10/17 → … |
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Dive into the research topics of 'Recurrent Assistance: Cross-Dataset Training of LSTMs on Kitchen Tasks'. Together they form a unique fingerprint.Projects
- 1 Finished
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LOCATE: LOcation adaptive Constrained Activity recognition using Transfer learning
Damen, D. (Principal Investigator)
4/07/16 → 3/05/18
Project: Research