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From:
Julio Cezar Silveira Jacques Junior <[log in to unmask]>
Date:
Sun, 20 Dec 2020 19:33:04 +0100
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We cordially invite you to participate in our CVPR 2021 Chalearn Looking at
People Large Scale Signer Independent Isolated SLR Challenge and Sign
Language Recognition in the Wild Workshop.

CVPR’2021 Large Scale Signer Independent Isolated SLR Challenge:
http://chalearnlap.cvc.uab.es/challenge/43/description/

We are organizing a sign classification challenge on a new dataset, named
AUTSL, consisting of 226 sign categories belonging to Turkish Sign Language
and 36,302 isolated sign video samples that are performed by 43 different
signers. The dataset includes a variety of different backgrounds from
indoor and outdoor environments, lighting variability, different postures
of signers, dynamic backgrounds, high intra and low inter-class
variabilities. Two competition tracks will be available: RGB only, and
RGB+Depth classification.

The results of the challenge will be presented at the associated CVPR 2021
Chalearn workshop. Participants of the challenge will be also invited to
submit a paper to the workshop presenting their solutions.

Important Dates :

   -

   Start of the Challenge (development phase): December 22th, 2020
   -

   Start of test phase: March 3rd, 2021
   -

   End of the Challenge: March 11th, 2021
   -

   Release of final results: March 20th, 2021


CVPR’2021 Chalearn LAP Sign Language Recognition in the Wild Workshop

http://chalearnlap.cvc.uab.es/workshop/42/description/

There are several open challenges in Sign Language Recognition to develop
useful systems in practice, including signer independent evaluation,
continuous sign recognition, exploitation of hand contextual cues (face and
body), sign production, as well as model generalization to different sign
languages and demographics, to name a few. In this workshop, we would like
to bring together researchers from the related disciplines to discuss the
advances and the challenges in this field. In this context, we accept
papers addressing the issues related to, but not limited to, these topics:



   -

   Continuous SLR models
   -

   Isolated SLR models in unconstrained settings
   -

   Multi-modal SLR models
   -

   SLR datasets: design considerations, new proposals and analysis of
   existing datasets.
   -

   SLR production
   -

   Interpretability/explainability of SLR models
   -

   Few shot and unsupervised SLR
   -

   Fairness, accountability, and transparency in SLR
   -

   Lip reading
   -

   Egocentric SLR

Paper submission to the workshop is independent from challenge
participation.


Important Dates:

   -

   Paper Submission: March 28th, 2021
   -

   Decisions to Authors: April 10th, 2021
   -

   Camera-Ready: April, 16th, 2021


INVITED SPEAKERS

Juergen Gall, Computer Vision Group, Bonn, Germany

Dimitris Metaxas Rutgers, City of New Brunswick, United States of America

Antonis Argyros, University of Crete, Crete, Greece

Richard Bowden, University of Surrey, Guildford, United Kingdom

Natalia Neverova, Facebook AI Research

Bencie Woll, UCL Deafness Cognition and Language Research Centre

Hongdong Li, Australian National University

ORGANIZATION & SPONSORS

Sergio Escalera, Computer Vision Center (UAB) and University of Barcelona,
Spain

Hacer Yalim Keles, Ankara University, Turkey

Julio C. S. Jacques Junior, Universitat Oberta de Catalunya (UOC) and
Computer Vision Center (CVC/UAB), Spain

Ozge Mercanoglu Sincan, Ankara University, Turkey

Sponsors:

This event is sponsored by ChaLearn - http://chalearnlap.cvc.uab.es/

University of Barcelona, Computer Vision Center at Autonomous University of
Barcelona, Human Pose Recovery and Behavior Analysis group, and Ankara
University, Computer Vision and Machine Learning Group are the co-sponsors
of the  SLR Challenge.

-- 

Julio C. S. Jacques Junior
Estudis d'Informàtica, Multimèdia i Telecomunicació
*Postdoctoral Researcher*

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eimt.uoc.edu
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<https://twitter.com/UOCeimt> / linkedin
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<https://www.youtube.com/playlist?list=PL7BC27BBB1CB2A537>

[image: UOC] <http://www.uoc.edu/>

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