Mutual Support of Data Modalities in the Task of Sign Language Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43962807" target="_blank" >RIV/49777513:23520/21:43962807 - isvavai.cz</a>
Result on the web
<a href="https://openaccess.thecvf.com/content/CVPR2021W/ChaLearn/papers/Gruber_Mutual_Support_of_Data_Modalities_in_the_Task_of_Sign_CVPRW_2021_paper.pdf" target="_blank" >https://openaccess.thecvf.com/content/CVPR2021W/ChaLearn/papers/Gruber_Mutual_Support_of_Data_Modalities_in_the_Task_of_Sign_CVPRW_2021_paper.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPRW53098.2021.00381" target="_blank" >10.1109/CVPRW53098.2021.00381</a>
Alternative languages
Result language
angličtina
Original language name
Mutual Support of Data Modalities in the Task of Sign Language Recognition
Original language description
This paper presents a method for automatic sign language recognition that was utilized in the CVPR 2021 ChaLearn Challenge (RGB track). Our method is composed of several approaches combined in an ensemble scheme to perform isolated sign-gesture recognition. We combine modalities of video sample frames processed by a 3D ConvNet (I3D), with body-pose information in the form of joint locations processed by a Transformer, hand region images transformed into a semantic space, and linguistically defined locations of hands. Although the individual models perform sub-par (60% to 93% accuracy on validation data), the weighted ensemble results in 95.46% accuracy.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
ISBN
978-1-66544-899-4
ISSN
2160-7508
e-ISSN
2160-7516
Number of pages
10
Pages from-to
3419-3428
Publisher name
IEEE
Place of publication
Nashville
Event location
Virtual, Online
Event date
Jun 19, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000705890203056