no code implementations • 15 Mar 2024 • Ozge Mercanoglu Sincan, Necati Cihan Camgoz, Richard Bowden
Sign Language Translation (SLT) is a challenging task that aims to generate spoken language sentences from sign language videos.
no code implementations • 18 Aug 2023 • Ryan Wong, Necati Cihan Camgoz, Richard Bowden
In natural language processing (NLP) of spoken languages, word embeddings have been shown to be a useful method to encode the meaning of words.
no code implementations • 18 Aug 2023 • Ozge Mercanoglu Sincan, Necati Cihan Camgoz, Richard Bowden
Sign Language Translation (SLT) is a challenging task that aims to generate spoken language sentences from sign language videos, both of which have different grammar and word/gloss order.
1 code implementation • ICCV 2023 • Shubhra Aich, Jesus Ruiz-Santaquiteria, Zhenyu Lu, Prachi Garg, K J Joseph, Alvaro Fernandez Garcia, Vineeth N Balasubramanian, Kenrick Kin, Chengde Wan, Necati Cihan Camgoz, Shugao Ma, Fernando de la Torre
Our sampling scheme outperforms SOTA methods significantly on two 3D skeleton gesture datasets, the publicly available SHREC 2017, and EgoGesture3D -- which we extract from a publicly available RGBD dataset.
1 code implementation • CVPR 2022 • Ben Saunders, Necati Cihan Camgoz, Richard Bowden
To learn sign co-articulation, we propose a novel Frame Selection Network (FS-Net) that improves the temporal alignment of interpolated dictionary signs to continuous signing sequences.
no code implementations • 18 Feb 2022 • Matthew J. Vowels, Sina Akbari, Necati Cihan Camgoz, Richard Bowden
Unfortunately, they are unlikely to be sufficiently flexible to be able to adequately model real-world phenomena, and may yield biased estimates.
no code implementations • SLTAT (LREC) 2022 • Ben Saunders, Necati Cihan Camgoz, Richard Bowden
Recent approaches to Sign Language Production (SLP) have adopted spoken language Neural Machine Translation (NMT) architectures, applied without sign-specific modifications.
no code implementations • ICCV 2021 • Ben Saunders, Necati Cihan Camgoz, Richard Bowden
Using a progressive transformer for the translation sub-task, we propose a novel Mixture of Motion Primitives (MoMP) architecture for sign language animation.
no code implementations • 22 Jul 2021 • Ben Saunders, Necati Cihan Camgoz, Richard Bowden
To tackle SLVA, we propose AnonySign, a novel automatic approach for visual anonymisation of sign language data.
no code implementations • 21 Jul 2021 • Tao Jiang, Necati Cihan Camgoz, Richard Bowden
In this paper, we focus on the task of one-shot sign spotting, i. e. given an example of an isolated sign (query), we want to identify whether/where this sign appears in a continuous, co-articulated sign language video (target).
no code implementations • 5 May 2021 • Necati Cihan Camgoz, Ben Saunders, Guillaume Rochette, Marco Giovanelli, Giacomo Inches, Robin Nachtrab-Ribback, Richard Bowden
Computational sign language research lacks the large-scale datasets that enables the creation of useful reallife applications.
no code implementations • 23 Apr 2021 • Tao Jiang, Necati Cihan Camgoz, Richard Bowden
Skeletor can achieve this as it implicitly learns the spatio-temporal context of human motion via a transformer based neural network.
no code implementations • 16 Apr 2021 • Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
An important goal across most scientific fields is the discovery of causal structures underling a set of observations.
1 code implementation • CVPR 2021 • Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
Given that supervision is often expensive or infeasible to acquire, we choose to incorporate structural inductive bias and present an unsupervised, deep State-Space-Model for Video Disentanglement (VDSM).
no code implementations • 11 Mar 2021 • Ben Saunders, Necati Cihan Camgoz, Richard Bowden
Sign languages are multi-channel visual languages, where signers use a continuous 3D space to communicate. Sign Language Production (SLP), the automatic translation from spoken to sign languages, must embody both the continuous articulation and full morphology of sign to be truly understandable by the Deaf community.
no code implementations • 3 Mar 2021 • Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
Causal reasoning is a crucial part of science and human intelligence.
no code implementations • 19 Nov 2020 • Ben Saunders, Necati Cihan Camgoz, Richard Bowden
To be truly understandable and accepted by Deaf communities, an automatic Sign Language Production (SLP) system must generate a photo-realistic signer.
1 code implementation • 28 Sep 2020 • Matthew James Vowels, Necati Cihan Camgoz, Richard Bowden
Undertaking causal inference with observational data is incredibly useful across a wide range of tasks including the development of medical treatments, advertisements and marketing, and policy making.
no code implementations • 28 Sep 2020 • Matthew James Vowels, Necati Cihan Camgoz, Richard Bowden
Undertaking causal inference with observational data is extremely useful across a wide range of domains including the development of medical treatments, advertisements and marketing, and policy making.
no code implementations • 1 Sep 2020 • Necati Cihan Camgoz, Oscar Koller, Simon Hadfield, Richard Bowden
Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore.
no code implementations • 27 Aug 2020 • 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.
1 code implementation • ECCV 2020 • 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.
1 code implementation • CVPR 2020 • Necati Cihan Camgoz, Oscar Koller, Simon Hadfield, Richard Bowden
We report state-of-the-art sign language recognition and translation results achieved by our Sign Language Transformers.
no code implementations • CVPR 2020 • Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
Two outer VAEs with shared weights attempt to reconstruct the input and infer a latent space, whilst a nested VAE attempts to reconstruct the latent representation of one image, from the latent representation of its paired image.
no code implementations • 15 Nov 2019 • Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
However, there is some debate about how to encourage disentanglement with VAEs and evidence indicates that existing implementations of VAEs do not achieve disentanglement consistently.
1 code implementation • CVPR 2018 • Necati Cihan Camgoz, Simon Hadfield, Oscar Koller, Hermann Ney, Richard Bowden
SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language.
2 code implementations • ICCV 2017 • Necati Cihan Camgoz, Simon Hadfield, Oscar Koller, Richard Bowden
We propose a novel deep learning approach to solve simultaneous alignment and recognition problems (referred to as "Sequence-to-sequence" learning).
Ranked #16 on Sign Language Recognition on RWTH-PHOENIX-Weather 2014