mlsanityData to 14 Oct 2026

Modelling the Distribution of Human Motion for Sign Language Assessment

arXiv 2408.10073 ↗First posted 19 Aug 2024cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Sign Language Assessment (SLA) tools are useful to aid in language learning and are underdeveloped. Previous work has focused on isolated signs or comparison against a single reference video to assess Sign Languages (SL). This paper introduces a novel SLA tool designed to evaluate the comprehensibility of SL by modelling the natural distribution of human motion. We train our pipeline on data from native signers and evaluate it using SL learners. We compare our results to ratings from a human raters study and find strong correlation between human ratings and our tool. We visually demonstrate our tools ability to detect anomalous results spatio-temporally, providing actionable feedback to aid in SL learning and assessment.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 24 of its 60 references that are in the map. First posted on arXiv 19 Aug 2024.

Builds on

24 of 60 references.

References · 24

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  3. 2016Variational Inference: A Review for Statisticianscited by 1,472
  4. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  5. 2019MediaPipe: A Framework for Building Perception Pipelinescited by 583
  6. 2015Variational Dropout and the Local Reparameterization Trickcited by 490
  7. 2020Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translationcited by 256
  8. 2020Progressive Transformers for End-to-End Sign Language Productioncited by 120
  9. 2022FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessmentcited by 107
  10. 2023Gloss-free Sign Language Translation: Improving from Visual-Language Pretrainingcited by 105
  11. 2019Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videoscited by 104
  12. 2021Skeleton Aware Multi-modal Sign Language Recognitioncited by 101
12 more
  1. 2024LLMs are Good Sign Language Translatorscited by 77
  2. 2024Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translationcited by 57
  3. 2023Gloss Attention for Gloss-free Sign Language Translationcited by 51
  4. 2021OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languagescited by 31
  5. 2023SignDiff: Diffusion Model for American Sign Language Productioncited by 25
  6. 2024A Data-Driven Representation for Sign Language Productioncited by 17
  7. 2023Improving 3D Pose Estimation for Sign Languagecited by 16
  8. 2022Changing the Representation: Examining Language Representation for Neural Sign Language Productioncited by 15
  9. 2020Action Quality Assessment using Siamese Network-Based Deep Metric Learningcited by 12
  10. 2024Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced Datasetscited by 4
  11. 2024Learning to Score Sign Language with Two-stage Methodcited by 2
  12. 2024Motion Capture Analysis of Verb and Adjective Types in Austrian Sign Languagecited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Meaningful Pose-Based Sign Language Evaluationcited by 6
  2. 2026SignAgent: Agentic LLMs for Linguistically-Grounded Sign Language Annotation and Dataset Curationcited by 1
  3. 2026BackTranslation2.0 -- A Linguistically Motivated Metric to Assess Sign Language Productioncited by 1