One Model is Not Enough: Ensembles for Isolated 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%2F22%3A43966108" target="_blank" >RIV/49777513:23520/22:43966108 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/1424-8220/22/13/5043/htm" target="_blank" >https://www.mdpi.com/1424-8220/22/13/5043/htm</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s22135043" target="_blank" >10.3390/s22135043</a>
Alternative languages
Result language
angličtina
Original language name
One Model is Not Enough: Ensembles for Isolated Sign Language Recognition
Original language description
In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze two appearance-based approaches, I3D and TimeSformer, and one pose-based approach, SPOTER. The appearance-based approaches are trained on a few different data modalities, whereas the performance of SPOTER is evaluated on different types of preprocessing. All the methods are tested on two publicly available datasets: AUTSL and WLASL300. We experiment with ensemble techniques to achieve new state-of-the-art results of 73.84% accuracy on the WLASL300 dataset by using the CMA-ES optimization method to find the best ensemble weight parameters. Furthermore, we present an ensembling technique based on the Transformer model, which we call Neural Ensembler.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
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
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
SENSORS
ISSN
1424-8220
e-ISSN
1424-8220
Volume of the periodical
22
Issue of the periodical within the volume
13
Country of publishing house
CH - SWITZERLAND
Number of pages
17
Pages from-to
nestrankovano
UT code for WoS article
000824167200001
EID of the result in the Scopus database
2-s2.0-85133217387