37 citations · 70 across the 16 of their papers we have counts for
20 papers
POET: Prompt Offset Tuning for Continual Human Action Adaptation
Prachi Garg, Joseph K J, Vineeth N Balasubramanian +6
As extended reality (XR) is redefining how users interact with computing devices, research in human action recognition is gaining prominence. Typically, models deployed on immersiv…
Towards Privacy-Aware Sign Language Translation at Scale
Phillip Rust, Bowen Shi, Skyler Wang +2
A major impediment to the advancement of sign language translation (SLT) is data scarcity. Much of the sign language data currently available on the web cannot be used for training…
Is context all you need? Scaling Neural Sign Language Translation to Large Domains of Discourse
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…
Learnt Contrastive Concept Embeddings for Sign Recognition
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. Sign languages are visual languages,…
Hierarchical I3D for Sign Spotting
Ryan Wong, Necati Cihan Camgöz, Richard Bowden
Most of the vision-based sign language research to date has focused on Isolated Sign Language Recognition (ISLR), where the objective is to predict a single sign class given a shor…
Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production
Ben Saunders, Necati Cihan Camgoz, Richard Bowden
Sign languages are visual languages, with vocabularies as rich as their spoken language counterparts. However, current deep-learning based Sign Language Production (SLP) models pro…
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics
Matthew J. Vowels, Sina Akbari, Necati Cihan Camgoz +1
Parameter estimation in empirical fields is usually undertaken using parametric models, and such models readily facilitate statistical inference. Unfortunately, they are unlikely t…
Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production
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. In add…
Looking for the Signs: Identifying Isolated Sign Instances in Continuous Video Footage
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 continuou…
Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives
Ben Saunders, Necati Cihan Camgoz, Richard Bowden
It is common practice to represent spoken languages at their phonetic level. However, for sign languages, this implies breaking motion into its constituent motion primitives. Avata…
AnonySIGN: Novel Human Appearance Synthesis for Sign Language Video Anonymisation
Ben Saunders, Necati Cihan Camgoz, Richard Bowden
The visual anonymisation of sign language data is an essential task to address privacy concerns raised by large-scale dataset collection. Previous anonymisation techniques have eit…
Content4All Open Research Sign Language Translation Datasets
Necati Cihan Camgoz, Ben Saunders, Guillaume Rochette +4
Computational sign language research lacks the large-scale datasets that enables the creation of useful reallife applications. To date, most research has been limited to prototype…
Shadow-Mapping for Unsupervised Neural Causal Discovery
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. Unfortunately, causal discovery methods which are based on co…
Skeletor: Skeletal Transformers for Robust Body-Pose Estimation
Tao Jiang, Necati Cihan Camgoz, Richard Bowden
Predicting 3D human pose from a single monoscopic video can be highly challenging due to factors such as low resolution, motion blur and occlusion, in addition to the fundamental a…
Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition
Amit Moryossef, Ioannis Tsochantaridis, Joe Dinn +6
Signed languages are visual languages produced by the movement of the hands, face, and body. In this paper, we evaluate representations based on skeleton poses, as these are explai…
Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks
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 s…
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. We provide a review…
VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Experts
Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
Disentangled representations support a range of downstream tasks including causal reasoning, generative modeling, and fair machine learning. Unfortunately, disentanglement has been…
Everybody Sign Now: Translating Spoken Language to Photo Realistic Sign Language Video
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. Prior approaches based on gr…
Targeted VAE: Variational and Targeted Learning for Causal Inference
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…