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Found 27 papers, 8 papers with code
Using an LLM to Turn Sign Spottings into Spoken Language Sentences
Sign Language Translation (SLT) is a challenging task that aims to generate spoken language sentences from sign language videos.
Is context all you need? Scaling Neural Sign Language Translation to Large Domains of Discourse
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.
Learnt Contrastive Concept Embeddings for Sign Recognition
In natural language processing (NLP) of spoken languages, word embeddings have been shown to be a useful method to encode the meaning of words.
Data-Free Class-Incremental Hand Gesture Recognition
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.
Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production
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.
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics
Unfortunately, they are unlikely to be sufficiently flexible to be able to adequately model real-world phenomena, and may yield biased estimates.
Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production
Recent approaches to Sign Language Production (SLP) have adopted spoken language Neural Machine Translation (NMT) architectures, applied without sign-specific modifications.
Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives
Using a progressive transformer for the translation sub-task, we propose a novel Mixture of Motion Primitives (MoMP) architecture for sign language animation.
AnonySIGN: Novel Human Appearance Synthesis for Sign Language Video Anonymisation
To tackle SLVA, we propose AnonySign, a novel automatic approach for visual anonymisation of sign language data.
Looking for the Signs: Identifying Isolated Sign Instances in Continuous Video Footage
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).
Content4All Open Research Sign Language Translation Datasets
Computational sign language research lacks the large-scale datasets that enables the creation of useful reallife applications.
Skeletor: Skeletal Transformers for Robust Body-Pose Estimation
Skeletor can achieve this as it implicitly learns the spatio-temporal context of human motion via a transformer based neural network.
Shadow-Mapping for Unsupervised Neural Causal Discovery
An important goal across most scientific fields is the discovery of causal structures underling a set of observations.
VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Experts
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).
Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks
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.
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
Causal reasoning is a crucial part of science and human intelligence.
Everybody Sign Now: Translating Spoken Language to Photo Realistic Sign Language Video
To be truly understandable and accepted by Deaf communities, an automatic Sign Language Production (SLP) system must generate a photo-realistic signer.
Targeted VAE: Variational and Targeted Learning for Causal Inference
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.
Targeted VAE: Structured Inference and Targeted Learning for Causal Parameter Estimation
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.
Multi-channel Transformers for Multi-articulatory Sign Language Translation
Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore.
Adversarial Training for Multi-Channel Sign Language Production
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.
Progressive Transformers for End-to-End Sign Language Production
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.
Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation
We report state-of-the-art sign language recognition and translation results achieved by our Sign Language Transformers.
NestedVAE: Isolating Common Factors via Weak Supervision
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.
Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement
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.
Neural Sign Language Translation
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.
SubUNets: End-To-End Hand Shape and Continuous Sign Language Recognition
We propose a novel deep learning approach to solve simultaneous alignment and recognition problems (referred to as "Sequence-to-sequence" learning).
Ranked #14 on
Sign Language Recognition
on RWTH-PHOENIX-Weather 2014
