Sign language segmentation with temporal convolutional networks
This work is significant for improving the accuracy of sign language segmentation, which is crucial for developing better sign language translation and recognition systems for the deaf community.
This paper addresses the problem of identifying temporal boundaries between signs in continuous sign language videos. The proposed method significantly improves upon prior state-of-the-art results on the BSLCORPUS, PHOENIX14, and BSL-1K datasets.
The objective of this work is to determine the location of temporal boundaries between signs in continuous sign language videos. Our approach employs 3D convolutional neural network representations with iterative temporal segment refinement to resolve ambiguities between sign boundary cues. We demonstrate the effectiveness of our approach on the BSLCORPUS, PHOENIX14 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.