ECCV 2022 Sign Spotting Challenge Call for Papers

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

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 tracks:

    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 intervals. 

Challenge webpage: 

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
independently of their rank position.


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

Jose L. Alba-Castro , 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 Enri´quez, atlanTTic research center, 
      University of Vigo, Spain