Name
Abstract | ||
|---|---|---|
In this work, we propose a framework to collect a large-scale, diverse sign language dataset that can be used to train automatic sign language recognition models. The first contribution of this work is SDTRACK, a generic method for signer tracking and diarisation in the wild. Our second contribution is SEEHEAR, a dataset of 90 hours of British Sign Language (BSL) content featuring more than 1000 signers, and including interviews, monologues and debates. Using SDTRACK, the SEEHEAR dataset is annotated with 35K active signing tracks, with corresponding signer identities and subtitles, and 40K automatically localised sign labels. As a third contribution, we provide benchmarks for signer diarisation and sign recognition on SEEHEAR. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414856 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Signer Diarisation, Sign Language Datasets | Conference | 1 |
PageRank | References | Authors |
0.36 | 7 | 14 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel Albanie | 1 | 40 | 9.91 |
| gul varol | 2 | 243 | 10.32 |
| Liliane Momeni | 3 | 1 | 1.37 |
| Triantafyllos Afouras | 4 | 121 | 9.19 |
| Andrew D. Brown | 5 | 216 | 43.94 |
| Chuhan Zhang | 6 | 2 | 1.40 |
| Ernesto Coto | 7 | 2 | 0.73 |
| Necati Cihan Camgöz | 8 | 39 | 9.23 |
| Ben Saunders | 9 | 1 | 1.03 |
| Abhishek Dutta | 10 | 2 | 0.72 |
| Neil Fox | 11 | 1 | 1.03 |
| Richard Bowden | 12 | 1840 | 118.50 |
| Bencie Woll | 13 | 1 | 0.36 |
| Andrew Zisserman | 14 | 45998 | 3200.71 |