# Learnt Contrastive Concept Embeddings for Sign Recognition

arXiv: https://arxiv.org/abs/2308.09515 · HTML: https://mlsanity.com/paper/2308.09515/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [MediaPipe Hands: On-device Real-time Hand Tracking](https://mlsanity.com/paper/2006.10214/) (2020)
- [MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language](https://mlsanity.com/paper/1812.01053/) (2018)
- [BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues](https://mlsanity.com/paper/2007.12131/) (2020)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Visual Alignment Constraint for Continuous Sign Language Recognition](https://mlsanity.com/paper/2104.02330/) (2021)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [Multi-channel Transformers for Multi-articulatory Sign Language Translation](https://mlsanity.com/paper/2009.00299/) (2020)
- [SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition](https://mlsanity.com/paper/2110.05382/) (2021)
- [BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization](https://mlsanity.com/paper/2302.05075/) (2023)
- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)
- [Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition](https://mlsanity.com/paper/2104.10166/) (2021)
- [Automatic dense annotation of large-vocabulary sign language videos](https://mlsanity.com/paper/2208.02802/) (2022)
- [Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble](https://mlsanity.com/paper/2110.06161/) (2021)
- [Towards Zero-shot Sign Language Recognition](https://mlsanity.com/paper/2201.05914/) (2022)

## Built on by (most cited first, 5 of 5)

- [Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2405.04164/) (2024)
- [SignRep: Enhancing Self-Supervised Sign Representations](https://mlsanity.com/paper/2503.08529/) (2025)
- [SAGE: Segment-Aware Gloss-Free Encoding for Token-Efficient Sign Language Translation](https://mlsanity.com/paper/2507.09266/) (2025)
- [SegSLR: Promptable Video Segmentation for Isolated Sign Language Recognition](https://mlsanity.com/paper/2509.10710/) (2025)
- [Gloss-Free Representation Learning for Cross-Dataset Sign Spotting](https://mlsanity.com/paper/2608.11332/) (2026)

## Abstract (arXiv)

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, which require sign embeddings to capture the visual and linguistic semantics of sign. Unlike many common approaches to Sign Recognition, we focus on explicitly creating sign embeddings that bridge the gap between sign language and spoken language. We propose a learning framework to derive LCC (Learnt Contrastive Concept) embeddings for sign language, a weakly supervised contrastive approach to learning sign embeddings. We train a vocabulary of embeddings that are based on the linguistic labels for sign video. Additionally, we develop a conceptual similarity loss which is able to utilise word embeddings from NLP methods to create sign embeddings that have better sign language to spoken language correspondence. These learnt representations allow the model to automatically localise the sign in time. Our approach achieves state-of-the-art keypoint-based sign recognition performance on the WLASL and BOBSL datasets.