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Communication Dans Un Congrès Année : 2021

Sign Segmentation with Changepoint-Modulated Pseudo-Labelling

Résumé

The objective of this work is to find temporal boundaries between signs in continuous sign language. Motivated by the paucity of annotation available for this task, we propose a simple yet effective algorithm to improve segmentation performance on unlabelled signing footage from a domain of interest. We make the following contributions: (1) We motivate and introduce the task of source-free domain adaptation for sign language segmentation, in which labelled source data is available for an initial training phase, but is not available during adaptation. (2) We propose the Changepoint-Modulated Pseudo-Labelling (CMPL) algorithm to leverage cues from abrupt changes in motion-sensitive feature space to improve pseudo-labelling quality for adaptation. (3) We showcase the effectiveness of our approach for category-agnostic sign segmentation, transferring from the BSLCORPUS to the BSL-1K and RWTH-PHOENIX-Weather 2014 datasets, where we outperform the prior state of the art.

Dates et versions

hal-03513415 , version 1 (05-01-2022)

Identifiants

Citer

Katrin Renz, Nicolaj C. Stache, Neil Fox, Gül Varol, Samuel Albanie. Sign Segmentation with Changepoint-Modulated Pseudo-Labelling. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jun 2021, Nashville, TN, United States. ⟨10.1109/CVPRW53098.2021.00379⟩. ⟨hal-03513415⟩
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