We cordially invite you to participate in our ECCV’2022 Sign Spotting

Challenge description: To advance and motivate the research on Sign
Language Recognition (SLR), the challenge will use a partially annotated
continuous sign language dataset of more than 10 hours of video data in the
health domain and will address the challenging problem of fine-grain sign
spotting in continuous SLR. In this context, we want to put a spotlight on
the strengths and limitations of the existing approaches, and define the
future directions of the field. It will be divided in two competition


   Multiple Shot Supervised Learning (MSSL) is a classical machine learning
   Track where signs to be spotted are the same in training, validation and
   test sets. The three sets will contain samples of signs cropped from the
   continuous stream of Spanish sign language, meaning that all of them have
   co-articulation influence. The training set contains the begin-end
   timestamps annotated by a deaf person and a SL-interpreter with a
   homogeneous criterion of multiple instances for each of the query signs.
   Participants will need to spot those signs in a set of validation videos
   with captured annotations. The signers in the test set can be the same or
   different to the training and validation set. Signers are men, women, right
   and left-handed.


   One Shot Learning and Weak Labels (OSLWL) is a realistic variation of a
   one-shot learning problem adapted to the sign language specific problem,
   where it is relatively easy to obtain a couple of examples of a sign, using
   just a sign language dictionary, but it is much more difficult to find
   co-articulated versions of that specific sign. When subtitles are
   available, as in broadcast-based datasets, the typical approach consists of
   using the text to predict a likely interval where the sign might be
   performed. So in this track we simulate that case by providing a set of
   queries (isolated signs) and a set of video intervals around each and every
   co-articulated instance of the queries. Intervals with no instances of
   queries are also provided as negative groundtruth. Participants will need
   to spot the exact location of the sign instances in the provided video

Challenge webpage: https://chalearnlap.cvc.uab.cat/challenge/49/description/

Tentative Schedule:


   Start of the Challenge (development phase): April 20, 2022

   Start of test phase: June 17, 2022

   End of the Challenge: June 24, 2022

   Release of final results: July 1st, 2022

Participants are invited to submit their contributions to the associated
ECCV’22 Workshop (https://chalearnlap.cvc.uab.cat/workshop/50/description/),
independently of their rank position.


Sergio Escalera <[log in to unmask]>, Computer Vision
Center (CVC) and University of Barcelona, Spain

Jose L. Alba-Castro <[log in to unmask]>, atlanTTic research center,
University of Vigo, Spain

Thomas B. Moeslund, Aalborg University, Aalborg, Denmark

Julio C. S. Jacques Junior, Computer Vision Center (CVC), Spain

Manuel Vázquez Enrı́quez, atlanTTic research center, University of Vigo,

*Dr. Sergio Escalera Guerrero*
Full Professor at Universitat de Barcelona
ELLIS Fellow / Head of Human Pose Recovery and Behavior Analysis group /
ICREA Academia / Project Manager at the Computer Vision Center
Email: [log in to unmask] / Webpage:
http://www.sergioescalera.com/ <http://www.maia.ub.es/~sergio/>  / Phone:+34



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