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: https://chalearnlap.cvc.uab.cat/challenge/49/description/
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.
ORGANIZATION and CONTACT
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, Spain
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