| Paper ID | IVMSP-23.1 |
| Paper Title |
SIGN LANGUAGE SEGMENTATION WITH TEMPORAL CONVOLUTIONAL NETWORKS |
| Authors |
Katrin Renz, Nicolaj Stache, University of Heilbronn, Germany; Samuel Albanie, University of Oxford, United Kingdom; Gül Varol, Ecole des Ponts, France |
| Session | IVMSP-23: Applications 1 |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
The objective of this work is to determine the location of temporal boundaries between signs in continuous sign language. Our approach marries 3D convolutional neural network representations with iterative temporal segment refinement to resolve ambiguities between boundary cues. We demonstrate the effectiveness of our approach for on the BSLCorpus, Phoenix2014 and BSL-1K datasets, showing considerable improvement over the prior state of the art and the ability to generalise to new signers, languages and domains. |
