13 papers
Multi-channel Transformers for Multi-articulatory Sign Language Translation
Necati Cihan Camgoz, Oscar Koller, Simon Hadfield +1
Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore. In this pa…
Adversarial Training for Multi-Channel Sign Language Production
Ben Saunders, Necati Cihan Camgoz, Richard Bowden
Sign Languages are rich multi-channel languages, requiring articulation of both manual (hands) and non-manual (face and body) features in a precise, intricate manner. Sign Language…
Progressive Transformers for End-to-End Sign Language Production
Ben Saunders, Necati Cihan Camgoz, Richard Bowden
The goal of automatic Sign Language Production (SLP) is to translate spoken language to a continuous stream of sign language video at a level comparable to a human translator. If t…
Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation
Necati Cihan Camgoz, Oscar Koller, Simon Hadfield +1
Prior work on Sign Language Translation has shown that having a mid-level sign gloss representation (effectively recognizing the individual signs) improves the translation performa…
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning
Jaime Spencer, Richard Bowden, Simon Hadfield
In the current monocular depth research, the dominant approach is to employ unsupervised training on large datasets, driven by warped photometric consistency. Such approaches lack…
Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance
Jaime Spencer, Richard Bowden, Simon Hadfield
"Like night and day" is a commonly used expression to imply that two things are completely different. Unfortunately, this tends to be the case for current visual feature representa…