Recurrent Assistance: Cross-Dataset Training of LSTMs on Kitchen Tasks

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

9 Citations (Scopus)
380 Downloads (Pure)


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 languageEnglish
Title of host publication2017 IEEE International Conference of Computer Vision Workshop (ICCVW 2017)
Subtitle of host publicationProceedings of a meeting held 22-29 October 2017, Venice, Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)9781538610343
ISBN (Print)9781538610350
Publication statusPublished - Feb 2018
EventInternational Conference on Computer Vision Workshops (ICCVW), - Venice, Italy
Duration: 22 Oct 2017 → …

Publication series

ISSN (Print)2473-9444


ConferenceInternational Conference on Computer Vision Workshops (ICCVW),
Period22/10/17 → …


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