Sign languages are spatial-temporal languages and constitute a key form of communication for Deaf communities. Recent progress in fine-grained gesture and action classification, machine translation and image captioning, point to the possibility of automatic sign language understanding becoming a reality. The study of isolated sign recognition has a rich history in the computer vision community stretching back over thirty years. Thanks to the recent availability of larger datasets, researchers are now focusing on continuous sign language recognition, sentence alignment to continuous signing and sign language translation. Advances in generative networks are also enabling progress on sign language production, where written language is converted into sign language video.

The "Sign Language Recognition, Translation & Production" (SLRTP) Workshop brings together researchers working on different aspects of vision-based sign language research (including body posture, hands and face) and sign language linguists. The focus of this workshop is to broaden participation in sign language research from the computer vision community. We hope to identify important future research directions, and to cultivate collaborations. The workshop will consist of invited talks and also a challenge with three tracks: individual sign recognition; English sentence to sign sequence alignment; and sign spotting.

Challenge

The challenge will have three tracks. The first track is (1) sign recognition from co-articulated signing for a large number of classes – the task is to classify individual signs in continuous signing sequences given their approximate temporal extent. This should encourage discussion on how to best (i) exploit complementary signals across different modalities and articulators, (ii) model temporal information, (iii) account for long-tailed distributions.

The second track is for (2) alignment of spoken language sentences to continuous signing – the task of determining the temporal extent of a signing sequence, given its English translation. This is a key step for automatically constructing a parallel corpus for sign language translation. This should encourage discussion on how to best model video and text jointly.

The final track is (3) sign spotting : here the task is to identify whether and when a sign is performed in a given window of continuous signing. Sign spotting has a range of applications including: indexing of signing content to enable efficient search and “intelligent fast-forward” to topics of interest, automatic sign language dataset construction and “wake-word” recognition for signers.

Teams that submit their results to the challenges will also be required to submit a description of their systems. At the workshop, we will invite presentations from the challenge winners.

Dates

    Challenge open:
    TBA, 2022
    Challenge close:
    TBA, 2022
    Winners announced:
    TBA, 2022
    Workshop date:
    TBA, 2022

Keynotes

Melissa Malzkuhn

Melissa
Malzkuhn

Founder
Motion Light Lab

Mark Wheatley

Mark
Wheatley

Executive Director
European Union of the Deaf

Sarah Ebling

Sarah
Ebling

Senior researcher
University of Zurich

Jeff McWhinney

Jeff
McWhinney

Founder
SignVideo

Schedule

Organizers

Liliane Momeni

Liliane
Momeni

PhD Student
University of Oxford

Gul Varol

Gül
Varol

Assistant Professor
École des Ponts ParisTech

Samuel Albanie

Samuel
Albanie

Assistant Professor
University of Cambridge

Hannah Bull

Hannah
Bull

PhD Student
University of Paris-Saclay

Prajwal KR

Prajwal
KR

PhD Student
University of Oxford

Cihan Camgoz

Neil
Fox

Research Assistant
DCAL

Ben Saunders

Ben
Saunders

PhD Student
University of Surrey

Cihan Camgoz

Necati Cihan
Camgöz

Research Fellow
University of Surrey

Richard Bowden

Richard
Bowden

Professor
University of Surrey

Andrew Zisserman

Andrew
Zisserman

Professor
University of Oxford

Bencie Woll

Bencie
Woll

Professor
DCAL