Abstract
The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36, 302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories.
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 |
Number of pages | 10 |
Publisher | IEEE Computer Society Press |
Publication date | Sept 2021 |
Pages | 3467-3476 |
ISBN (Electronic) | 9781665448994 |
DOIs | |
Publication status | Published - Sept 2021 |
Externally published | Yes |
Event | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, United States Duration: 19 Jun 2021 → 25 Jun 2021 |
Conference
Conference | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 19/06/2021 → 25/06/2021 |
Series | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
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ISSN | 2160-7508 |
Bibliographical note
Funding Information:This work has been partially supported by the Scientific and Technological Research Council of Turkey (TUBITAK) project 217E022, the Spanish project PID2019-105093GB-I00 (MINECO/FEDER, UE) and CERCA Programme/Generalitat de Catalunya, and ICREA under the ICREA Academia programme.
Publisher Copyright:
© 2021 IEEE.