TY - GEN
T1 - Cross Transferring Activity Recognition to Word Level Sign Language Detection
AU - Radhakrishnan, Srijith
AU - Mohan, Nikhil C.
AU - Varma, Manisimha
AU - Varma, Jaithra
AU - Pai, Smitha N.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The lack of large scale labelled datasets in word-level sign language recognition (WSLR) poses a challenge to detecting sign language from videos. Most WSLR approaches operate on datasets that do not model real-world settings very well, as they do not have a high degree of variability in terms of signers, background, lighting and inter signer variation. We chose the MS-ASL dataset to overcome these limitations as they model open-world settings very well. This paper benchmarks successful action recognition architectures on the MS-ASL dataset using transfer learning. We have achieved new state-of-the-art accuracy (92.35%) with an improvement of 7.03% over the previous state-of-the-art introduced by the MS-ASL paper. We have analyzed how action-recognition architectures fair in the task of WSLR, and we propose SlowFast 8×8 ResNet 101 as a robust and suitable architecture for the task of WSLR.
AB - The lack of large scale labelled datasets in word-level sign language recognition (WSLR) poses a challenge to detecting sign language from videos. Most WSLR approaches operate on datasets that do not model real-world settings very well, as they do not have a high degree of variability in terms of signers, background, lighting and inter signer variation. We chose the MS-ASL dataset to overcome these limitations as they model open-world settings very well. This paper benchmarks successful action recognition architectures on the MS-ASL dataset using transfer learning. We have achieved new state-of-the-art accuracy (92.35%) with an improvement of 7.03% over the previous state-of-the-art introduced by the MS-ASL paper. We have analyzed how action-recognition architectures fair in the task of WSLR, and we propose SlowFast 8×8 ResNet 101 as a robust and suitable architecture for the task of WSLR.
UR - http://www.scopus.com/inward/record.url?scp=85137801923&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137801923&partnerID=8YFLogxK
U2 - 10.1109/CVPRW56347.2022.00273
DO - 10.1109/CVPRW56347.2022.00273
M3 - Conference contribution
AN - SCOPUS:85137801923
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 2445
EP - 2452
BT - Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
PB - IEEE Computer Society
T2 - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Y2 - 19 June 2022 through 20 June 2022
ER -