Projects per year
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.
|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)|
|Number of pages||9|
|Publication status||Published - Feb 2018|
|Event||International Conference on Computer Vision Workshops (ICCVW), - Venice, Italy|
Duration: 22 Oct 2017 → …
|Conference||International Conference on Computer Vision Workshops (ICCVW),|
|Period||22/10/17 → …|
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- 1 Finished
4/07/16 → 3/05/18